Community Multiscale Air Quality Simulations Research Articles

Expanding the simulation of East Asian super dust storms: physical transport mechanisms impacting the western Pacific

Abstract. Dust models are widely applied over the East Asian region for the simulation of dust emission, transport, and deposition. However, due to the uncertainties in estimates of dust transport, these methods still lack the necessary precision to capture the complexity of transboundary dust events. This study demonstrates an improvement in the Community Multiscale Air Quality (CMAQ) model dust treatment during long-range transport of dust from northwestern China to the South China Sea (SCS). To accomplish this, we considered a super dust storm (SDS) event in March 2010 and evaluated the dust scheme by including adjustments to the recent calibration (Dust_Refined_1) and bulk density (Dust_Refined_2) refinements individually and in combination (Dust_Refined_3). The Dust_Refined_3 normalized mean bias of PM10 was −30.65 % for the 2010 SDS event, which was lower in magnitude compared to Dust_Refined_1 (−41.18 %) and Dust_Refined_2 (−49.88 %). Indeed, Dust_Refined_3 improved the simulated aerosol optical depth (AOD) value during significant dust cases, e.g., in March 2005, March 2006, and April 2009. Dust_Refined_3 also showed more clearly that, in March 2010, a “double plume” (i.e., one plume originating from the Taiwan Strait and the other from the western Pacific) separated by the Central Mountain Range (CMR) of Taiwan affected dust transport on the island of Dongsha in the SCS. On 15–21 April 2021, both CMAQ simulations and satellite data highlighted the influence of Typhoon Surigae on dust transport to downwind Taiwan and the western Pacific Ocean (WPO). The CMAQ Dust_Refined_3 simulations further revealed that many dust aerosols were removed over the WPO due to Typhoon Surigae. Hence, the model indicated a near-zero dust particle concentration over the WPO, which was significantly different from previous dust transport episodes over the Taiwan region. Therefore, our study suggested an effective method to improve dust management of CMAQ under unique topographical and meteorological conditions.

Adverse Health Impacts of Outdoor Air Pollution, Including from Wildland Fires, in the United States: "Health of the Air," 2018-2020.

Rationale: Adverse health impacts from outdoor air pollution occur across the United States, but the magnitude of these impacts varies widely by geographic region. Ambient pollutant concentrations, emission sources, baseline health conditions, and population sizes and distributions are all important factors that need to be taken into account to quantify local health burdens. Objectives: To determine health impacts from ambient air pollution concentrations in the United States that exceed the levels recommended by the American Thoracic Society. Methods: Using a methodology that has been well established in previous "Health of the Air" reports, this study provides policy-relevant estimates for every monitored county and city in the United States for the adverse health impacts of outdoor pollution concentrations using U.S. Environmental Protection Agency design values for years 2018-2020. Additionally, for the first time, the report includes adverse birth outcomes as well as estimates of health impacts specifically attributable to wildland fires using an exposure dataset generated through Community Multiscale Air Quality simulations. Results: The adverse health burdens attributable to air pollution occur across the entire age spectrum, including adverse birth outcomes (10,660 preterm and/or low-weight births; 95% confidence interval [CI], 3,180-18,330), in addition to mortality impacts (21,300 avoidable deaths; 95% CI, 16,180-26,200), lung cancer incidence (3,000 new cases; 95% CI, 1,550-4,390), multiple types of cardiovascular and respiratory morbidity (748,660 events; 95% CI, 326,050-1,057,080), and adversely impacted days (52.4 million days; 95% CI, 7.9-92.4 million days). Two different estimates of mortality impacts from wildland fires were created based on assumptions regarding the underlying toxicity of particles from wildland fires (low estimate of 4,080 deaths, 95% CI, 240-7,890; middle estimate of 28,000 deaths, 95% CI, 27,300-28,700). Conclusions: This year's report identified sizable health benefits that would be expected to occur across the United States with compliance with more health-protective air quality standards such as those recommended by the American Thoracic Society. This study also indicates that a large number of excess deaths are attributable to emissions from wildland fires; air quality management strategies outside what is required by the Clean Air Act will be needed to best address this important source of air pollution and its associated health risks.

Ozone air quality deteriorated by inter-provincial transport downwind of Seoul metropolitan area

While ozone air quality has continuously improved in major cities worldwide, it has recently degraded in cities in East Asia, including South Korea. This study aims to examine one possible mechanism for an ozone episode during the summertime that is the deteriorated air quality by inter-provincial transport. We analyzed an ozone episode that occurred in the Seoul metropolitan area and its surrounding areas during early summer. We used the Community Multiscale Air Quality (CMAQ) model, employing the Process Analysis (PA) method, to estimate the contributions of inter-provincial transport of ozone and its precursors from the metropolitan area to downwind cities. First of all, the observation data showed that the daily maximum 1-h average ozone (O3) concentrations in the surrounding cities exceeded 100 ppb for at least three consecutive days from June 2nd to 6th, 2019. In the cluster analysis of diurnal patterns, it was evident that the daily maximum O3 concentration increased from the metropolitan area to the downwind cities, with peak times occurred up to 2-h later. In the PA results of surface O3 concentration in the CMAQ simulations, a significant downward diffusion of ozone was observed in Seoul during the daytime due to the strong chemical reduction of ozone (i.e., NO titration). On the other hand, Chuncheon and Wonju, the major downwind cities, exhibited a significant positive contribution of vertical transport to surface O3 concentration as the boundary layer develops in the morning. The ozone in the residual layer at night was primarily produced in the metropolitan area and ,then, transported to downwind cities through inter-provincial transport. Consequently, this implies that the inter-provincial transport of ozone in the atmospheric boundary layer plays an important role in the formation of a multi-day ozone episode in the downwind cities of metropolitan area.

The impact of altering emission data precision on compression efficiency and accuracy of simulations of the community multiscale air quality model

Abstract. The Community Multiscale Air Quality (CMAQ) model has been a vital tool for air quality research and management at the United States Environmental Protection Agency (US EPA) and at government environmental agencies and academic institutions worldwide. The CMAQ model requires a significant amount of disk space to store and archive input and output files. For example, an annual simulation over the contiguous United States (CONUS) with horizontal grid-cell spacing of 12 km requires 2–3 TB of input data and can produce anywhere from 7–45 TB of output data, depending on modeling configuration and desired post-processing of the output (e.g., for evaluations or graphics). After a simulation is complete, model data are archived for several years, or even decades, to ensure the replicability of conducted research. As a result, careful disk space management is essential to optimize resources and ensure the uninterrupted progress of ongoing research and applications requiring large-scale, air quality modeling. Proper disk-space management may include applying optimal data-compression techniques that are executed on input and output files for all CMAQ simulations. There are several (not limited to) such utilities that compress files using lossless compression, such as GNU Gzip (gzip) and Basic Leucine Zipper Domain (bzip2). A new approach is proposed in this study that reduces the precision of the emission input for air quality modeling to reduce storage requirements (after a lossless compression utility is applied) and accelerate runtime. The new approach is tested using CMAQ simulations and post-processed CMAQ output to examine the impact on the performance of the air quality model. In total, four simulations were conducted, and nine cases were post-processed from direct simulation output to determine disk-space efficiency, runtime efficiency, and model (predictive) accuracy. Three simulations were run with emission input containing only five, four, or three significant digits. To enhance the analysis of disk-space efficiency, the output from the altered precision emission CMAQ simulations were additionally post-processed to contain five, four, or three significant digits. The fourth, and final, simulation was run using the full precision emission files with no alteration. Thus, in total, 13 gridded products (4 simulations and 9 altered precision output cases) were analyzed in this study. Results demonstrate that the altered precision emission files reduced the disk-space footprint by 6 %, 25 %, and 48 % compared to the unaltered emission files when using the bzip2 compression utility for files containing five, four, or three significant digits, respectively. Similarly, the altered output files reduced the required disk space by 19 %, 47 %, and 69 % compared to the unaltered CMAQ output files when using the bzip2 compression utility for files containing five, four, or three significant digits, respectively. For both compressed datasets, bzip2 performed better than gzip, in terms of compression size, by 5 %–27 % for emission data and 15 %–28 % for CMAQ output for files containing five, four, or three significant digits. Additionally, CMAQ runtime was reduced by 2 %–7 % for simulations using emission files with reduced precision data in a non-dedicated environment. Finally, the model-estimated pollutant concentrations from the four simulations were compared to observed data from the US EPA Air Quality System (AQS) and the Ammonia Monitoring Network (AMoN). Model performance statistics were impacted negligibly. In summary, by reducing the precision of CMAQ emission data to five, four, or three significant digits, the simulation runtime in a non-dedicated environment was slightly reduced, disk-space usage was substantially reduced, and model accuracy remained relatively unchanged compared to the base CMAQ simulation, which suggests that the precision of the emission data could be reduced to more efficiently use computing resources while minimizing the impact on CMAQ simulations.

Improved estimation of particulate matter in China based on multisource data fusion

Particulate matter (PM) is a global health concern and causes millions of premature deaths worldwide annually. High-resolution and full-coverage PM datasets are essential to support the accurate assessment of PM exposure. Here, a three-stage model framework is developed based on the Community Multiscale Air Quality (CMAQ) simulations (12 km) and multisource data fusion to estimate 1 km daily PM concentrations across China in 2015, including PM2.5 (<2.5 μm) and PM10 (<10 μm). The three-stage model performs well with cross-validation coefficient of determination (R2) of 0.91 and 0.87, and root mean square error (RMSE) of 17.3 μg/m3 and 27.2 μg/m3 for PM2.5 and PM10, respectively. After data fusion from multiple sources, the concentrations of PM2.5 and PM10 are in better agreement with ground observations compared to the CMAQ simulation with RMSE reduced by 72 % and 67 %. High PM2.5 events mainly occur in the North China Plain, Yangtze River Delta, and Sichuan Basin, and PM10 show similar spatial patterns to PM2.5 in eastern China. These full-coverage PM datasets enable in-depth analysis of PM pollution over small areas and support future epidemiological studies and health assessments.

Pollution and transport of atmospheric PAHs in Xinjiang: Observation at a high-altitude background site combined with numerical simulation

As an essential energy production base in China, the pollution of PAHs in Xinjiang cannot be ignored. However, this region has limited studies on the overall distribution and transport characteristics of polycyclic aromatic hydrocarbons (PAHs). This study used a combination of high-altitude background site (MTtian) observations and Community Multiscale Air Quality simulations for systematic investigation. The simulation results revealed that the distribution of PAHs in Xinjiang was high in the north and low in the south, primarily associated with emissions. Moreover, the observed concentration of PAHs in the MTtian was 1.37 ng/m3, which lies between the clean and polluted background sites, emphasizing the importance of regional anthropogenic emissions. The observed PAH concentrations at MTtian was 32.2% higher when the air mass was from Urumqi than those at other places. This is related to mountain-valley winds, as shown by the simulations. In addition, the transport of high-altitude PAHs in Xinjiang is primarily affected by westerly winds. This mechanism can explain the highly simulated PAHs at high altitudes in the Tarim Basin. The PAHs emitted from Xinjiang were transported eastward downstream, with Mongolia being the most affected region, contributing to 7.33% of the total PAHs.

A New Aerosol Dry Deposition Model for Air Quality and Climate Modeling

AbstractDry deposition of aerosols from the atmosphere is an important but poorly understood and inadequately modeled process in atmospheric systems for climate and air quality. Comparisons of currently used aerosol dry deposition models to a compendia of published field measurement studies in various landscapes show very poor agreement over a wide range of particle sizes. In this study, we develop and test a new aerosol dry deposition model that is a modification of the current model in the Community Multiscale Air Quality (CMAQ) model. The new model agrees much better with measured dry deposition velocities across particle sizes. The key innovation is the addition of a second inertial impaction term for microscale obstacles such as leaf hairs, microscale ridges, and needleleaf edge effects. The most significant effect of the new model is to increase the mass dry deposition of the accumulation mode aerosols in CMAQ. Accumulation mode mass dry deposition velocities increase by almost an order of magnitude in forested areas with lesser increases for shorter vegetation. Peak PM2.5 concentrations are reduced in some forested areas by up to 40% in CMAQ simulations. Over the continuous United States, the new model reduced PM2.5 by an average of 16% for July 2018 at the Air Quality System monitoring sites. For summer 2018 simulations, bias and error of PM2.5 concentrations are significantly reduced, especially in forested areas.

Improving the particle dry deposition scheme in the CMAQ photochemical modeling system

Dry deposition of atmospheric aerosols in large-scale models is a critical, but highly uncertain, sink process with a strong dependence on particle size, meteorological conditions, and land surface properties. This study investigates the particle dry deposition scheme implemented in the standard Community Multiscale Air Quality (CMAQ) model v5.2.1, characterizes its underlying parameterized components with comparison to a similar scheme in a contemporary regional-scale model, and proposes two updated schemes that are then evaluated with available ambient particle deposition velocity (Vd) measurements. Both updated schemes reduce the surprisingly strong dependence of deposition velocity on the aerosol mode width, with one scheme further introducing a dependence on vegetation coverage that is broadly consistent with variability in observations between vegetated and non-vegetated surfaces. Compared to the base scheme, the updated scheme with vegetation dependence increases Vd for submicron particles and decreases it for larger particles by an average of 37% and −66%, respectively. This scheme performs statistically better than the base scheme, reducing fractional biases by 56%–97% for vegetated land-use types and has roughly equivalent performance over water. The base and updated schemes are tested with three annual CMAQ (v5.2.1) simulations for the year 2011; predicted ambient aerosol concentrations are evaluated with routine monitoring network observations and predicted dry deposition fluxes are evaluated with data from the Clean Air Status and Trends Network (CASTNET). The updated scheme with vegetation dependence reduces negative fractional biases for PM10 by 41% and positive fractional biases for PM2.5 organic carbon by 15%. This scheme has been incorporated into the most recent publicly accessible versions of CMAQ (v5.3 and beyond) to replace the scheme used in previous versions of CMAQ (v4.5 through v5.2.1).

Investigation of the Community Multiscale air quality (CMAQ) model representation of the Climate Penalty Factor (CPF)

Elevated levels of tropospheric ozone (O3) caused by anthropogenic emissions of NOx and VOCs have a negative impact on human health, crops, and ecosystems. Even if precursor emissions are reduced compared to current levels, predicted higher temperatures due to increased greenhouse gas emissions could impede resulting air quality benefits. The Climate Penalty Factor (CPF) has been devised to quantify the adverse impact of increasing temperatures on surface O3. We compute the CPF from surface observations at CASTNET (mostly rural) and AQS (mostly urban) sites and compare these values to CMAQ output for the contiguous U.S. and seven sub-regions for the O3 season (May 1 through September 30) for each year from 2002 through 2012. Here we show that CMAQ can in general reproduce with reasonable accuracy the CPF for the CONUS. In predominantly rural regions (CASTNET) observed CPFs are 1.94 ppb/°C while simulated CPFs are 2.11 ppb/°C. Observed CPFs are 2.00 ppb/°C in predominantly urban regions (AQS), lower than simulated CPFs of 2.39 ppb/°C. CMAQ reproduces CPFs at rural sites reasonably well but tends to somewhat overestimate CPFs at urban sites. Upon examination of each region, the CMAQ simulation for the Southeast shows a substantial overestimate (43%) of CPF for urban (AQS) sites. Conversely, the CMAQ simulations for the Northeast show a slight underestimate (8%) of the CPF at rural (CASTNET) sites. Both results for CPF (overestimated for urban sites and either underestimated or close to observed for rural sites) could be related to the tendency of CMAQ with CB05-TUCL (an excessively long lifetime of alkyl nitrates with respect to photolysis) to overestimate NOx or keep it too close to the urban source locations. Our analysis suggests that CMAQ may attribute a somewhat greater fraction of O3 production to an increase in temperature than observed. Finally, there is a ∼2% per year decrease in CPF throughout the CONUS during the 11-year period in both measurements and the CMAQ simulations, suggesting that reductions in NOx emissions can mitigate the impact of climate change on surface ozone.

A novel hybrid clustering model of region segmentation to fuse CMAQ simulations with observations

The Community Multiscale Air Quality (CMAQ) model can predict air pollutant concentrations at every pre-determined location but usually suffers from considerable systematic bias. By post-process the CAMQ simulations with ground observations, K-means-cluster-Observation-examiner-Boundary-denoiser (KOB) model was developed as an unsupervised hybrid model, to segment regions by capturing spatial dependencies of CMAQ time-series and systematically reduce the bias of CMAQ simulations. The KOB model was then applied in 2019 to improve the CMAQ predictions of respirable suspended particulates (RSP) concentrations in the Pearl River Delta (PRD) region of southern China. Compared to the CMAQ simulations, the KOB model is implemented with hourly CMAQ simulations and significantly improves the 1-h predictions of RSP at all stations, with the index of agreement (IOA) increasing from 0.70 to 0.86 and average root mean square error (RMSE) reducing from 28.4 to 20.0 μg/m3. A better agreement was also achieved between the monthly average RSP from predictions using the KOB model and ground observations, with IOA increased from 0.87 to 0.93 and RMSE decreased from 12.4 to 8.2 μg/m3. In addition, the capability of the KOB model to predict extreme pollution was greatly improved, with the RMSE reduced from 76.3 to 50.8 μg/m3. Furthermore, the improvement was identified not only for the next 1-h predictions, but also for the predictions in the next 6 h and after 6 h the SBC method outperforms the KOB model. Through data fusion, our hybrid clustering model can achieve robust real-time air quality forecasting.

Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations.

3D-grid-based chemical transport models, such as the Community Multiscale Air Quality (CMAQ) modeling system, have been widely used for predicting concentrations of ambient air pollutants. However, typical horizontal resolutions of nationwide CMAQ simulations (12 × 12 km2) cannot capture local-scale gradients for accurately assessing human exposures and environmental justice disparities. In this study, a Bayesian ensemble machine learning (BEML) framework, which integrates 13 learning algorithms, was developed for downscaling CMAQ estimates of ozone daily maximum 8 h averages to the census tract level, across the contiguous US, and was demonstrated for 2011. Three-stage hyperparameter tuning and targeted validations were designed to ensure the ensemble model's ability to interpolate, extrapolate, and capture concentration peaks. The Shapley value metric from coalitional game theory was applied to interpret the drivers of subgrid gradients. The flexibility (transferability) of the 2011-trained BEML model was further tested by evaluating its ability to estimate fine-scale concentrations for other years (2012-2017) without retraining. To demonstrate the feasibility of using the BEML approach to strictly "data-limited" situations, the model was applied to downscale CMAQ outputs for a future-year scenario-based simulation that considers effects of variations in meteorology associated with climate change.

Improvement of PM2.5 and O3 forecasting by integration of 3D numerical simulation with deep learning techniques

Air pollution is a major impediment to the sustainable development of cities and society. Governed by emission characteristics and meteorological conditions, the formation and destruction of fine particulate matter (PM2.5) and ozone (O3) are complicated, and accurate predictions of the concentrations of these two major secondary atmospheric pollutants remain challenging. In this study, by combining meteorological and air pollutant data from ground observations and the Weather Research and Forecasting (WRF)-Community Multiscale Air Quality (CMAQ) model simulations, a deep learning model structure based on long short-term memory layers (LSTM) was developed and applied to predict the PM2.5 and O3 concentrations in the future 48 h period. The forecasting improvement was extended to the whole Greater Bay Area by introducing a spatial correction (SC) method to the CMAQ simulation results. Compared with the original CMAQ forecast, the new method gained a 26% reduction in mean absolute error (MAE) and a 33% reduction in root mean square error (RMSE), respectively, in terms of PM2.5; it also achieved a 40% reduction in MAE and a 34% reduction in RMSE in terms of O3. SC method, applied to the whole GBA region, also reduced the overall MAE and RMSE by 10% and 17% in terms of PM2.5 and by 31% and 25% in terms of O3, respectively. Using an AI approach, our study provides new perspectives for further improving air quality forecasting from both temporal and spatial perspectives, thus increasing the smartness and resilience of the cities and promoting environmentally sustainable development in the area.

Predicting the Nonlinear Response of PM2.5 and Ozone to Precursor Emission Changes with a Response Surface Model.

Reducing PM2.5 and ozone concentrations is important to protect human health and the environment. Chemical transport models, such as the Community Multiscale Air Quality (CMAQ) model, are valuable tools for exploring policy options for improving air quality but are computationally expensive. Here, we statistically fit an efficient polynomial function in a response surface model (pf-RSM) to CMAQ simulations over the eastern U.S. for January and July 2016. The pf-RSM predictions were evaluated using out-of-sample CMAQ simulations and used to examine the nonlinear response of air quality to emission changes. Predictions of the pf-RSM are in good agreement with the out-of-sample CMAQ simulations, with some exceptions for cases with anthropogenic emission reductions approaching 100%. NOX emission reductions were more effective for reducing PM2.5 and ozone concentrations than SO2, NH3, or traditional VOC emission reductions. NH3 emission reductions effectively reduced nitrate concentrations in January but increased secondary organic aerosol (SOA) concentrations in July. More work is needed on SOA formation under conditions of low NH3 emissions to verify the responses of SOA to NH3 emission changes predicted here. Overall, the pf-RSM performs well in the eastern U.S., but next-generation RSMs based on deep learning may be needed to meet the computational requirements of typical regulatory applications.

The Community Multiscale Air Quality (CMAQ) model versions 5.3 and 5.3.1: system updates and evaluation.

The Community Multiscale Air Quality (CMAQ) model version 5.3 (CMAQ53), released to the public in August 2019 and followed by version 5.3.1 (CMAQ531) in December 2019, contains numerous science updates, enhanced functionality, and improved computation efficiency relative to the previous version of the model, 5.2.1 (CMAQ521). Major science advances in the new model include a new aerosol module (AERO7) with significant updates to secondary organic aerosol (SOA) chemistry, updated chlorine chemistry, updated detailed bromine and iodine chemistry, updated simple halogen chemistry, the addition of dimethyl sulfide (DMS) chemistry in the CB6r3 chemical mechanism, updated M3Dry bidirectional deposition model, and the new Surface Tiled Aerosol and Gaseous Exchange (STAGE) bidirectional deposition model. In addition, support for the Weather Research and Forecasting (WRF) model's hybrid vertical coordinate (HVC) was added to CMAQ53 and the Meteorology-Chemistry Interface Processor (MCIP) version 5.0 (MCIP50). Enhanced functionality in CMAQ53 includes the new Detailed Emissions Scaling, Isolation and Diagnostic (DESID) system for scaling incoming emissions to CMAQ and reading multiple gridded input emission files. Evaluation of CMAQ531 was performed by comparing monthly and seasonal mean daily 8 h average (MDA8) O3 and daily PM2.5 values from several CMAQ531 simulations to a similarly configured CMAQ521 simulation encompassing 2016. For MDA8 O3, CMAQ531 has higher O3 in the winter versus CMAQ521, due primarily to reduced dry deposition to snow, which strongly reduces wintertime O3 bias (2-4 ppbv monthly average). MDA8 O3 is lower with CMAQ531 throughout the rest of the year, particularly in spring, due in part to reduced O3 from the lateral boundary conditions (BCs), which generally increases MDA8 O3 bias in spring and fall ( 0.5 μg m-3). For daily 24 h average PM2.5, CMAQ531 has lower concentrations on average in spring and fall, higher concentrations in summer, and similar concentrations in winter to CMAQ521, which slightly increases bias in spring and fall and reduces bias in summer. Comparisons were also performed to isolate updates to several specific aspects of the modeling system, namely the lateral BCs, meteorology model version, and the deposition model used. Transitioning from a hemispheric CMAQ (HCMAQ) version 5.2.1 simulation to a HCMAQ version 5.3 simulation to provide lateral BCs contributes to higher O3 mixing ratios in the regional CMAQ simulation in higher latitudes during winter (due to the decreased O3 dry deposition to snow in CMAQ53) and lower O3 mixing ratios in middle and lower latitudes year-round (due to reduced O3 over the ocean with CMAQ53). Transitioning from WRF version 3.8 to WRF version 4.1.1 with the HVC resulted in consistently higher (1.0-1.5 ppbv) MDA8 O3 mixing ratios and higher PM2.5 concentrations (0.1-0.25 μg m-3) throughout the year. Finally, comparisons of the M3Dry and STAGE deposition models showed that MDA8 O3 is generally higher with M3Dry outside of summer, while PM2.5 is consistently higher with STAGE due to differences in the assumptions of particle deposition velocities to non-vegetated surfaces and land use with short vegetation (e.g., grasslands) between the two models. For ambient NH3, STAGE has slightly higher concentrations and smaller bias in the winter, spring, and fall, while M3Dry has higher concentrations and smaller bias but larger error and lower correlation in the summer.

Enhancement of the polynomial functions response surface model for real-time analyzing ozone sensitivity

Quantification of the nonlinearities between ambient ozone (O3) and the emissions of nitrogen oxides (NOx) and volatile organic compound (VOC) is a prerequisite for an effective O3 control strategy. An Enhanced polynomial functions Response Surface Model (Epf-RSM) with the capability to analyze O3-NOx-VOC sensitivities in real time was developed by integrating the hill-climbing adaptive method into the optimized Extended Response Surface Model (ERSM) system. The Epf-RSM could single out the best suited polynomial function for each grid cell to quantify the responses of O3 concentrations to precursor emission changes. Several comparisons between Epf-RSM and pf-ERSM (polynomial functions based ERSM) were performed using out-of-sample validation, together with comparisons of the spatial distribution and the Empirical Kinetic Modeling Approach diagrams. The comparison results showed that Epf-RSM effectively addressed the drawbacks of pf-ERSM with respect to over-fitting in the margin areas and high biases in the transition areas. The O3 concentrations predicted by Epf-RSM agreed well with Community Multi-scale Air Quality simulation results. The case study results in the Pearl River Delta and the north-western area of the Shandong province indicated that the O3 formations in the central areas of both the regions were more sensitive to anthropogenic VOC in January, April, and October, while more NOx-sensitive in July.

Evaluation of CMAQ modeling sensitivity to planetary boundary layer parameterizations for gaseous and particulate pollutants over a fjord valley

Abstract Three-dimensional chemical transport models are useful for spatial and temporal analyses of outdoor air quality. However, the suitability of boundary-layer parameterizations for air pollution modeling over deep, coastal valleys has seldom been tested. An evaluation of the Community Multiscale Air Quality (CMAQ) model performance for five planetary boundary-layer schemes (PBL) with the Weather Research and Forecasting (WRF) meteorological driver was conducted at 1-km horizontal resolution for fine particulate matter (PM2.5), sulfur dioxide (SO2) and nitrogen dioxide (NO2) over the Terrace-Kitimat valley of northwestern British Columbia, Canada. The top-ranked schemes were Mellor-Yamada-Nakanishi-Niino Level 3 (MYNN3) for PM2.5 and Mellor-Yamada-Janjic for NO2. Both schemes ranked high for absolute SO2 levels, but the MYJ and Asymmetric Convective Model, version 2 (ACM2) schemes qualitatively emulated peak summertime diurnal concentrations in the near field of elevated point sources. Greater nighttime SO2 concentrations with MYNN3 and Yonsei University PBL schemes, in less agreement with station monitoring 8 km downwind of emissions from tall stacks, suggested sustained pollutant mixing and downward transport within the nocturnal boundary layer. Consequently, for these two schemes with representations of nonlocal mass flux transfers between model layers, inland penetrations of pollutant plumes were farther than those of ACM2, MYJ, and University of Washington schemes. For NO2 and PM2.5 that mainly discharged passively from fugitive, ground-level sources, hence are less accurately quantified than SO2 emissions, the fully local MYJ, and semi-local MYNN3 PBL schemes more reasonably reproduced peak season concentrations than other schemes. It is concluded that for air pollution modeling in rugged, remote areas, the mode of pollutant emissions is important for the choice of a PBL scheme. PM2.5 was consistently underestimated by the various PBL schemes, and aspects for improving CMAQ simulations for a complex environment are discussed.

Observation Constrained Aromatic Emissions in Shanghai, China

AbstractAromatics in the atmosphere are a large contributor to ozone and secondary organic aerosol formation and have an adverse impact on human health. Observation‐based emissions are urgently needed for air quality management and decision making due to the large uncertainties in the emissions inventories. Atmospheric aromatics were continuously observed at one urban site in Shanghai of China in 2015, which were compared with the simulations of the Community Multiscale Air Quality (CMAQ) model. Large differences were found in the temporal variations between the model and measurements, corroborating with previous studies that the current bottom‐up emission inventories are highly biased. We corrected the aromatic emissions biases of the emission inventories using the observations with the CMAQ simulations, with the assumption that the response relationship of the emissions input and aromatic concentrations was reasonable on a monthly basis in CMAQ simulations, suggesting that the results are dependent on the model performance. The resulting aromatic emissions exhibited clear monthly variations, increasing from 5.5 ± 2.1 Gg month−1 in February to 17.4 ± 7.7 Gg month−1 in July, being different from the flat monthly pattern in the current bottom‐up emissions inventory. Toluene, xylenes, and ethylbenzene dominated aromatic emissions, accounting for ~74% on average but with considerable monthly variations, which was different from the nearly uniform value of 77% throughout the whole year in the emissions inventory. These highly temporally resolved and speciated in situ aromatics measurements provide observation‐based constraints on aromatic emissions, which cannot be obtained from satellite measurements. This study proposes a top‐down emission estimation method constrained by ambient measurements.

Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues

Abstract. For the purpose of providing reliable and robust air quality predictions, an air quality prediction system was developed for the main air quality criteria species in South Korea (PM10, PM2.5, CO, O3 and SO2). The main caveat of the system is to prepare the initial conditions (ICs) of the Community Multiscale Air Quality (CMAQ) model simulations using observations from the Geostationary Ocean Color Imager (GOCI) and ground-based monitoring networks in northeast Asia. The performance of the air quality prediction system was evaluated during the Korea-United States Air Quality Study (KORUS-AQ) campaign period (1 May–12 June 2016). Data assimilation (DA) of optimal interpolation (OI) with Kalman filter was used in this study. One major advantage of the system is that it can predict not only particulate matter (PM) concentrations but also PM chemical composition including five main constituents: sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), organic aerosols (OAs) and elemental carbon (EC). In addition, it is also capable of predicting the concentrations of gaseous pollutants (CO, O3 and SO2). In this sense, this new air quality prediction system is comprehensive. The results with the ICs (DA RUN) were compared with those of the CMAQ simulations without ICs (BASE RUN). For almost all of the species, the application of ICs led to improved performance in terms of correlation, errors and biases over the entire campaign period. The DA RUN agreed reasonably well with the observations for PM10 (index of agreement IOA =0.60; mean bias MB =-13.54) and PM2.5 (IOA =0.71; MB =-2.43) as compared to the BASE RUN for PM10 (IOA =0.51; MB =-27.18) and PM2.5 (IOA =0.67; MB =-9.9). A significant improvement was also found with the DA RUN in terms of bias. For example, for CO, the MB of −0.27 (BASE RUN) was greatly enhanced to −0.036 (DA RUN). In the cases of O3 and SO2, the DA RUN also showed better performance than the BASE RUN. Further, several more practical issues frequently encountered in the air quality prediction system were also discussed. In order to attain more accurate ozone predictions, the DA of NO2 mixing ratios should be implemented with careful consideration of the measurement artifacts (i.e., inclusion of alkyl nitrates, HNO3 and peroxyacetyl nitrates – PANs – in the ground-observed NO2 mixing ratios). It was also discussed that, in order to ensure accurate nocturnal predictions of the concentrations of the ambient species, accurate predictions of the mixing layer heights (MLHs) should be achieved from the meteorological modeling. Several advantages of the current air quality prediction system, such as its non-static free-parameter scheme, dust episode prediction and possible multiple implementations of DA prior to actual predictions, were also discussed. These configurations are all possible because the current DA system is not computationally expensive. In the ongoing and future works, more advanced DA techniques such as the 3D variational (3DVAR) method and ensemble Kalman filter (EnK) are being tested and will be introduced to the Korean air quality prediction system (KAQPS).

Fine-Scale Columnar and Surface NOx Concentrations over South Korea: Comparison of Surface Monitors, TROPOMI, CMAQ and CAPSS Inventory

Fine-scale nitrogen oxide (NOx) concentrations over South Korea are examined using surface observations, satellite data and high-resolution model simulations based on the latest emission inventory. While accurate information on NOx emissions in South Korea is crucial to understanding regional air quality in the region, consensus on the validation of NOx emissions is lacking. We investigate the spatial and temporal variation in fine-scale NOx emission sources over South Korea. Surface observations and newly available fine-scale satellite data (TROPOspheric Monitoring Instrument; TROPOMI; 3.5 × 7 km2) are compared with the community multiscale air quality (CMAQ) model based on the clean air policy support system (CAPSS) 2016 emission inventory. The results show that the TROPOMI NO2 column densities agree well with the CMAQ simulations based on CAPSS emissions (e.g., R = 0.96 for June 2018). The surface observations, satellite data and model are consistent in terms of their spatial distribution, the overestimation over the Seoul Metropolitan Area and major point sources; however, the model tends to underestimate the surface concentrations during the cold season.

The benefits of lower ozone due to air pollution emission reductions (2002–2011) in the Eastern United States during extreme heat

ABSTRACTUsing the Community Multiscale Air Quality (CMAQ) model and the Benefits Mapping and Analysis Program – Community Edition (BenMAP-CE) tool, we estimate the benefits of anthropogenic emission reductions between 2002 and 2011 in the Eastern United States (US) with respect to surface ozone concentrations and ozone-related health and economic impacts, during a month of extreme heat, July 2011. Based on CMAQ simulations using emissions appropriate for 2002 and 2011, we estimate that emission reductions since 2002 likely prevented 10– 15 ozone exceedance days (using the 2011 maximum 8-hr average ozone standard of 75 ppbv) throughout the Ohio River Valley and 5– 10 ozone exceedance days throughout the Washington, DC – Baltimore, MD metropolitan area during this extremely hot month. CMAQ results were fed into the BenMAP-CE tool to determine the health and health-related economic benefits of anthropogenic emission reductions between 2002 and 2011. We estimate that the concomitant health benefits from the ozone reductions were significant for this anomalous month: 160–800 mortalities (95% confidence interval (CI): 70–1,010) were avoided in July 2011 in the Eastern U.S, saving an estimated $1.3–$6.6 billion (CI: $174 million–$15.5 billion). Additionally, we estimate that emission reductions resulted in 950 (CI: 90–2,350) less hospital admissions from respiratory symptoms, 370 (CI: 180–580) less hospital admissions for pneumonia, 570 (CI: 0–1650) less Emergency Room (ER) visits from asthma symptoms, 922,020 (CI: 469,960–1,370,050) less minor restricted activity days (MRADs), and 430,240 (CI: −280,350–963,190) less symptoms of asthma exacerbation during July 2011.Implications: We estimate the benefits of air pollution emission reductions on surface ozone concentrations and ozone-related impacts on human health and the economy between 2002 and 2011 during an extremely hot month, July 2011, in the eastern United States (US) using the CMAQ and BenMAP-CE models. Results suggest that, during July 2011, emission reductions prevented 10-15 ozone exceedance days in the Ohio River Valley and 5-10 ozone exceedance days in the Mid Atlantic; saved 160-800 lives in the Eastern US, saving $1.3 - $6.5 billion; and resulted in 950 less hospital admissions for respiratory symptoms, 370 less hospital admissions for pneumonia, 570 less Emergency Room visits for asthma symptoms, 922,020 less minor restricted activity days, and 430,240 less symptoms of asthma exacerbation.