The role of natural variability in projections of climate change impacts on U.S. ozone pollution

Climate change can impact air quality by altering the atmospheric conditions that determine pollutant concentrations. Over large regions of the U.S., projected changes in climate are expected to favor formation of ground-level ozone and aggravate associated health effects. However, modeling studies exploring air quality-climate interactions have often overlooked the role of natural variability, a major source of uncertainty in projections. Here we use the largest ensemble simulation of climate-induced changes in air quality generated to date to assess its influence on estimates of climate change impacts on U.S. ozone. We find that natural variability can significantly alter the robustness of projections of the future climate's effect on ozone pollution. In this study, a 15 year simulation length minimum is required to identify a distinct anthropogenic-forced signal. Therefore, we suggest that studies assessing air quality impacts use multidecadal simulations or initial condition ensembles. With natural variability, impacts attributable to climate may be difficult to discern before midcentury or under stabilization scenarios

Trace gas/aerosol boundary concentrations and their impacts on continental-scale AQMEII modeling domains

Copyright 2011 Elsevier B.V., All rights reserved.Over twenty modeling groups are participating in the Air Quality Model Evaluation International Initiative (AQMEII) in which a variety of mesoscale photochemical and aerosol air quality modeling systems are being applied to continental-scale domains in North America and Europe for 2006 full-year simulations for model inter-comparisons and evaluations. To better understand the reasons for differences in model results among these participating groups, each group was asked to use the same source of emissions and boundary concentration data for their simulations. This paper describes the development and application of the boundary concentration data for this AQMEII modeling exercise. The European project known as GEMS (Global and regional Earth-system Monitoring using Satellite and in-situ data) has produced global-scale re-analyses of air quality for several years, including 2006 (http://gems.ecmwf.int). The GEMS trace gas and aerosol data were made available at 3-hourly intervals on a regular latitude/longitude grid of approximately 1.9° resolution within 2 "cut-outs" from the global model domain. One cut-out was centered over North America and the other over Europe, covering sufficient spatial domain for each modeling group to extract the necessary time- and space-varying (horizontal and vertical) concentrations for their mesoscale model boundaries. Examples of the impact of these boundary concentrations on the AQMEII continental simulations are presented to quantify the sensitivity of the simulations to boundary concentrations. In addition, some participating groups were not able to use the GEMS data and instead relied upon other sources for their boundary concentration specifications. These are noted, and the contrasting impacts of other data sources for boundary data are presented. How one specifies four-dimensional boundary concentrations for mesoscale air quality simulations can have a profound impact on the model results, and hence, this aspect of data preparation must be performed with considerable care.Peer reviewedFinal Accepted Versio

Parallel processing and non-uniform grids in global air quality modeling

A large-scale global air quality model, running efficiently on a single vector processor, is enhanced to make more realistic and more long-term simulations feasible. Two strategies are combined: non-uniform grids and parallel processing. The communication through the hierarchy of non-uniform grids interferes with the inter-processor communication. We discuss load balance in the decomposition of the domain, I/O, and inter-processor communication. A model shows that the communication overhead for both techniques is very low, whence non-uniform grids allow for large speed-ups and high speed-up can be expected from parallelization. The implementation is in progress, and results of experiments will be reported elsewhere

A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2.5_{2.5} concentration

A typical problem in air pollution epidemiology is exposure assessment for individuals for which health data are available. Due to the sparsity of monitoring sites and the limited temporal frequency with which measurements of air pollutants concentrations are collected (for most pollutants, once every 3 or 6 days), epidemiologists have been moving away from characterizing ambient air pollution exposure solely using measurements. In the last few years, substantial research efforts have been placed in developing statistical methods or machine learning techniques to generate estimates of air pollution at finer spatial and temporal scales (daily, usually) with complete coverage. Some of these methods include: geostatistical techniques, such as kriging; spatial statistical models that use the information contained in air quality model outputs (statistical downscaling models); linear regression modeling approaches that leverage the information in GIS covariates (land use regression); or machine learning methods that mine the information contained in relevant variables (neural network and deep learning approaches). Although some of these exposure modeling approaches have been used in several air pollution epidemiological studies, it is not clear how much the predicted exposures generated by these methods differ, and which method generates more reliable estimates. In this paper, we aim to address this gap by evaluating a variety of exposure modeling approaches, comparing their predictive performance and computational difficulty. Using PM$_{2.5}$ in year 2011 over the continental U.S. as case study, we examine the methods' performances across seasons, rural vs urban settings, and levels of PM$_{2.5}$ concentrations (low, medium, high)

Relation of emissions to air quality for photochemical smog

Effective evaluation of air pollution control strategies requires the use of validated and reliable mathemtical models that can relate pollutant emissions to atmospheric air quality. The primary objective of this research program has been to develop a fundamental capability to assess the effectiveness of air pollution control measures in reducing photochemical air pollution. An important aspect of the development has been to simplify the preparation of input data and operational use of the resulting model. The system has been designed to be used by air pollution agencies with relatively little experience in atmospheric physics and chemistry. The assumptions commonly employed in model formulations have been evaluated to ensure a valid representation of the physical and chemical processes in the atmosphere. In the most recent phase of this research the comprehensive photochemical airshed model has been evaluated against data available in the South Coast Air Basin of Southern California. This task was undertaken in collaboration with the California Air Resources Board, Air Quality Modeling Section. A statistical analysis package has been used to evaluate the correspondence of predicted and observed concentrations for the days on which the model was evaluated. An assessment of the EPA ozone isopleth modeling technique has been initiated

Natural Variability in Projections of Climate Change Impacts on Fine Particulate Matter Pollution

Variations in meteorology associated with climate change can impact fine particulate matter (PM2.5) pollution by affecting natural emissions, atmospheric chemistry, and pollutant transport. However, substantial discrepancies exist among model-based projections of PM2.5 impacts driven by anthropogenic climate change. Natural variability can significantly contribute to the uncertainty in these estimates. Using a large ensemble of climate and atmospheric chemistry simulations, we evaluate the influence of natural variability on projections of climate change impacts on PM2.5 pollution in the United States. We find that natural variability in simulated PM2.5 can be comparable or larger than reported estimates of anthropogenic-induced climate impacts. Relative to mean concentrations, the variability in projected PM2.5 climate impacts can also exceed that of ozone impacts. Based on our projections, we recommend that analyses aiming to isolate the effect climate change on PM2.5 use 10 years or more of modeling to capture the internal variability in air quality and increase confidence that the anthropogenic-forced effect is differentiated from the noise introduced by natural variability. Projections at a regional scale or under greenhouse gas mitigation scenarios can require additional modeling to attribute impacts to climate change. Adequately considering natural variability can be an important step toward explaining the inconsistencies in estimates of climate-induced impacts on PM2.5. Improved treatment of natural variability through extended modeling lengths or initial condition ensembles can reduce uncertainty in air quality projections and improve assessments of climate policy risks and benefits

A DESIGN OF GAS MIXER FOR SYNGAS ENGINE USING THREE-DIMENSIONAL CFD MODELING.

A gas mixer prototype is developed for mixing air and synthesis gas or “syngas” as a fuel. Syngas is being recognized as a viable energy source worldwide, particularly for stationary power generation. Syngas has a very low energy density, so a mixer with λ (ratio of actual to stoichiometric air-fuel ratio) in the range of 1.1 to 1.7 is expected. In this study, three-dimensional computational fluid dynamics (CFD) modeling is used to design venturi mixer, coaxial mixer and coaxial mixer with vortex generator. CFD modeling is used to investigate and analyze the influence of the throat diameter, gas chamber thickness and gas exits diameter on mixer characteristics and performance of the venturi mixer. While on the coaxial mixer model, CFD is used to analyze the influences of the primary nozzle exit diameter, constant pressure mixing chamber geometry, constant area mixing chamber geometry, divergent passage geometry, syngas inlet position and primary nozzle exit position on the coaxial mixer characteristics and performance. To design appropriate vortex generator, computational models are used to analyze the influence of the mechanical tab angle, number of tabs and geometry on the mixing characteristics and performance of the coaxial mixer. Attention is focused on the effect of mixers and vortex generator tabs geometry on the air-fuel ratio, pressure loss and mixing quality. Based on the numerical results, an optimized design of venturi gas mixer, coaxial mixer and vortex generator is decided and made. The optimized design of the venturi mixer has λ in the range of 1.2 to 1.3, good mixing quality and pressure loss of 46 Pa on air flow rate 100 m 3 /h. The optimized design of the coaxial mixer has λ ranging from 1.1 to 1.7 corresponding to pressure losses from 28 to 19 Pa at 100 m 3 /h air-flow rate. The optimized design of coaxial mixer equipped with the proposed vortex generator has λ in the range of 1.1 to 1.7 corresponding to pressure loss in the range of 41.4 to 31.9 Pa at 100 m 3 /h air flow rate. At λ about 1.2 and 100 m 3 /h air flow rate, the mixing quality of the optimized venturi mixer, coaxial mixer and coaxial mixer equipped with vortex generator have coefficient of variation (CoV) of 0.67, 0.88 and 0.29 respectively

Impacts of household sources on air pollution at village and regional scales in India

Approximately 3 billion people worldwide cook with solid fuels, such as wood, charcoal, and agricultural residues. These fuels, also used for residential heating, are often combusted in inefficient devices, producing carbonaceous emissions. Between 2.6 and 3.8 million premature deaths occur as a result of exposure to fine particulate matter from the resulting household air pollution (Health Effects Institute, 2018a; World Health Organization, 2018). Household air pollution also contributes to ambient air pollution; the magnitude of this contribution is uncertain. Here, we simulate the distribution of the two major health-damaging outdoor air pollutants (PM2:5 and O3) using state-of-thescience emissions databases and atmospheric chemical transport models to estimate the impact of household combustion on ambient air quality in India. The present study focuses on New Delhi and the SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in the Palwal District of Haryana, located about 80 km south of New Delhi. The DDESS covers an approximate population of 200 000 within 52 villages. The emissions inventory used in the present study was prepared based on a national inventory in India (Sharma et al., 2015, 2016), an updated residential sector inventory prepared at the University of Illinois, updated cookstove emissions factors from Fleming et al. (2018b), and PM2:5 speciation from cooking fires from Jayarathne et al. (2018). Simulation of regional air quality was carried out using the US Environmental Protection Agency Community Multiscale Air Quality modeling system (CMAQ) in conjunction with the Weather Research and Forecasting modeling system (WRF) to simulate the meteorological inputs for CMAQ, and the global chemical transport model GEOS-Chem to generate concentrations on the boundary of the computational domain. Comparisons between observed and simulated O3 and PM2:5 levels are carried out to assess overall airborne levels and to estimate the contribution of household cooking emissions