A comparative study of two-way and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US: performance evaluation and impacts of chemistry-meteorology feedbacks on air quality

The two-way coupled Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) model has been developed to more realistically represent the atmosphere by accounting for complex chemistry-meteorology feedbacks. In this study, we present a comparative analysis of two-way (with consideration of both aerosol direct and indirect effects) and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US. Long-term (5 years from 2008 to 2012) simulations using WRF-CMAQ with both offline and two-way coupling modes are carried out with anthropogenic emissions based on multiple years of the U.S. National Emission Inventory and chemical initial and boundary conditions derived from an advanced Earth system model (i.e., a modified version of the Community Earth System Model/Community Atmospheric Model). The comprehensive model evaluations show that both two-way WRF-CMAQ and WRF-only simulations perform well for major meteorological variables such as temperature at 2 m, relative humidity at 2 m, wind speed at 10 m, precipitation (except for against the National Climatic Data Center data), and shortwave and longwave radiation. Both two-way and offline CMAQ also show good performance for ozone (O₃) and fine particulate matter (PM_2.5). Due to the consideration of aerosol direct and indirect effects, two-way WRF-CMAQ shows improved performance over offline coupled WRF and CMAQ in terms of spatiotemporal distributions and statistics, especially for radiation, cloud forcing, O₃, sulfate, nitrate, ammonium, elemental carbon, tropospheric O₃ residual, and column nitrogen dioxide (NO₂). For example, the mean biases have been reduced by more than 10 W m^-2 for shortwave radiation and cloud radiative forcing and by more than 2 ppb for max 8 h O₃. However, relatively large biases still exist for cloud predictions, some PM_2.5 species, and PM₁₀ that warrant follow-up studies to better understand those issues. The impacts of chemistry-meteorological feedbacks are found to play important roles in affecting regional air quality in the US by reducing domain-average concentrations of carbon monoxide (CO), O₃, nitrogen oxide (NO _x ), volatile organic compounds (VOCs), and PM_2.5 by 3.1% (up to 27.8%), 4.2% (up to 16.2%), 6.6% (up to 50.9%), 5.8% (up to 46.6%), and 8.6% (up to 49.1%), respectively, mainly due to reduced radiation, temperature, and wind speed. The overall performance of the two-way coupled WRF-CMAQ model achieved in this work is generally good or satisfactory and the improved performance for two-way coupled WRF-CMAQ should be considered along with other factors in developing future model applications to inform policy making.

Neighbourhood-scale dispersion of traffic-induced ultrafine particles in central London: WRF large eddy simulations

Traffic-generated ultrafine particles (UFPs) in the urban atmosphere have a high proportion of their composition comprised of semi-volatile compounds (SVOCs). The evaporation/condensation processes of these SVOCs can alter UFP number size distributions and play an important role in determining the fate of UFPs in urban areas. The neighbourhood-scale dispersion (over distances < 1 km) and evolution of traffic-generated UFPs for a real-world street network in central London was simulated by using the WRF-LES model (the large eddy simulation mode of the Weather Research and Forecasting modelling system) coupled with multicomponent microphysics. The neighbourhood scale dispersion of UFPs was significantly influenced by the spatial pattern of the real-world street emissions. Model output indicated the shrinkage of the peak diameter from the emitted profile to the downwind profile, due to an evaporation process during neighbourhood-scale dispersion. The dilution process and the aerosol microphysics interact with each other during the neighbourhood dispersion of UFPs, yielding model output that compares well with measurements made at a location downwind of an intense roadside source. The model captured the total SVOC concentrations well, with overestimations for gas concentrations and underestimations for particle concentrations, particularly of the lighter SVOCs. The contribution of the intense source, Marylebone Road (MR) in London, to concentrations at the downwind location (as estimated by a model scenario with emissions from MR only) is comparable with that of the rest of the street network (a scenario without emissions from MR), implying that both are important. An appreciable level of non-linearity is demonstrated for nucleation mode UFPs and medium range carbon SVOCs at the downwind receptor site.

Improving PM2.5 Forecasts in China Using an Initial Error Transport Model

Efforts of using data assimilation to improve PM_2.5 forecasts have been hindered by the limited number of species and incomplete vertical coverage in the observations. The common practice of initializing a chemical transport model (CTM) with assimilated initial conditions (ICs) may lead to model imbalances, which could confine the impacts of assimilated ICs within a day. To address this challenge, we introduce an initial error transport model (IETM) approach to improving PM_2.5 forecasts. The model describes the transport of initial errors by advection, diffusion, and decay processes and calculates the impacts of assimilated ICs separately from the CTM. The CTM forecasts with unassimilated ICs are then corrected by the IETM output. We implement our method to improve PM_2.5 forecasts over central and eastern China. The reduced root-mean-square errors for 1-, 2-, 3-, and 4-day forecasts during January 2018 were 51.2, 27.0, 16.4, and 9.4 μg m^-3, respectively, which are 3.2, 6.9, 8.6, and 10.4 times those by the CTM forecasts with assimilated ICs. More pronounced improvements are found for highly reactive PM_2.5 components. These and similar results for July 2017 suggest that our method can enhance and extend the impacts of the assimilated data without being affected by the imbalance issue.

Fine Particulate Air Pollution from Electricity Generation in the US: Health Impacts by Race, Income, and Geography

Electricity generation is a large contributor to fine particulate matter (PM_2.5) air pollution. However, the demographic distribution of the resulting exposure is largely unknown. We estimate exposures to and health impacts of PM_2.5 from electricity generation in the US, for each of the seven Regional Transmission Organizations (RTOs), for each US state, by income and by race. We find that average exposures are the highest for blacks, followed by non-Latino whites. Exposures for remaining groups (e.g., Asians, Native Americans, Latinos) are somewhat lower. Disparities by race/ethnicity are observed for each income category, indicating that the racial/ethnic differences hold even after accounting for differences in income. Levels of disparity differ by state and RTO. Exposures are higher for lower-income than for higher-income, but disparities are larger by race than by income. Geographically, we observe large differences between where electricity is generated and where people experience the resulting PM_2.5 health consequences; some states are net exporters of health impacts, other are net importers. For 36 US states, most of the health impacts are attributable to emissions in other states. Most of the total impacts are attributable to coal rather than other fuels.

Short-Term Impacts of the Aliso Canyon Natural Gas Blowout on Weather, Climate, Air Quality, and Health in California and Los Angeles

The Aliso Canyon (Porter Ranch), California, natural gas blowout lasted 112 days, from October 23, 2015 to February 11, 2016, releasing 97 100 metric tonnes of methane, 7300 tonnes of ethane, and a host of other hydrocarbons into the Southern California air. This study estimates the impacts of the leak on transient weather, climate, air quality, and health in California and the Los Angeles Basin using a nested global-through-local weather-climate-air quality computer model. Results suggest that the Aliso Canyon leak may have increased the mixing ratios of multiple emitted hydrocarbon gases throughout California. Subsequent gas-phase photochemistry increased the mixing ratios of additional byproducts, including carbon monoxide, formaldehyde, acetaldehyde, peroxyacetyl nitrate, and ozone. Increases in air temperatures aloft and lesser increases at the surface due to thermal-infrared radiation absorption by methane stabilized the air over much of California, slightly reducing clouds, precipitation, and near-surface wind speed with greater reductions in Los Angeles than in California. The reduction in precipitation, in particular, increased PM_2.5 concentration, with a greater increase in Los Angeles than in California. The higher PM_2.5 increased estimated premature mortality in California by +32 (9-54) to +43 (15-66), depending on the set of relative risks used. Despite higher PM_2.5 in Los Angeles due to the leak, premature mortalities there were more ambiguous, ranging from a mean decrease of -7 to a mean increase of +15, for 2 simulations with different resolution and boundary conditions. The remaining mortalities occurred in the Central Valley and San Francisco Bay Area. Ozone premature mortalities away from the leak increased by <1. The study did not evaluate potential health impacts, including cancers, immediately near the leak. As such, the Aliso Canyon leak affected temperatures, pollution, and health throughout California. Future leaks will also likely have impacts.

InMAP: A model for air pollution interventions

Mechanistic air pollution modeling is essential in air quality management, yet the extensive expertise and computational resources required to run most models prevent their use in many situations where their results would be useful. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations-the air pollution outcome generally causing the largest monetized health damages-attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical information from the output of a state-of-the-science chemical transport model and a variable spatial resolution computational grid to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. In comparisons run here, InMAP recreates comprehensive model predictions of changes in total PM2.5 concentrations with population-weighted mean fractional bias (MFB) of -17% and population-weighted R2 = 0.90. Although InMAP is not specifically designed to reproduce total observed concentrations, it is able to do so within published air quality model performance criteria for total PM2.5. Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. InMAP can be trained to run for any spatial and temporal domain given the availability of appropriate simulation output from a comprehensive model. The InMAP model source code and input data are freely available online under an open-source license.

Public Health Costs of Primary PM2.5 and Inorganic PM2.5 Precursor Emissions in the United States

Current methods of estimating the public health effects of emissions are computationally too expensive or do not fully address complex atmospheric processes, frequently limiting their applications to policy research. Using a reduced-form model derived from tagged chemical transport model (CTM) simulations, we present PM2.5 mortality costs per tonne of inorganic air pollutants with the 36 km × 36 km spatial resolution of source location in the United States, providing the most comprehensive set of such estimates comparable to CTM-based estimates. Our estimates vary by 2 orders of magnitude. Emission-weighted seasonal averages were estimated at $88,000-130,000/t PM2.5 (inert primary), $14,000-24,000/t SO2, $3,800-14,000/t NOx, and $23,000-66,000/t NH3. The aggregate social costs for year 2005 emissions were estimated at $1.0 trillion dollars. Compared to other studies, our estimates have similar magnitudes and spatial distributions for primary PM2.5 but substantially different spatial patterns for precursor species where secondary chemistry is important. For example, differences of more than a factor of 10 were found in many areas of Texas, New Mexico, and New England states for NOx and of California, Texas, and Maine for NH3. Our method allows for updates as emissions inventories and CTMs improve, enhancing the potential to link policy research to up-to-date atmospheric science.

Cleaning the air and improving health with hydrogen fuel-cell vehicles

Converting all U.S. onroad vehicles to hydrogen fuel-cell vehicles (HFCVs) may improve air quality, health, and climate significantly, whether the hydrogen is produced by steam reforming of natural gas, wind electrolysis, or coal gasification. Most benefits would result from eliminating current vehicle exhaust. Wind and natural gas HFCVs offer the greatest potential health benefits and could save 3700 to 6400 U.S. lives annually. Wind HFCVs should benefit climate most. An all-HFCV fleet would hardly affect tropospheric water vapor concentrations. Conversion to coal HFCVs may improve health but would damage climate more than fossil/electric hybrids. The real cost of hydrogen from wind electrolysis may be below that of U.S. gasoline.