Air Quality Model Simulations Research Articles

Atmospheric Dynamics and Ozone Cycle during Sea Breeze in a Mediterranean Complex Urbanized Coastal Site

AbstractPersistent high pressure conditions over the Mediterranean Basin favor the occurrence of sea breezes that can lead to ozone transport through complex recirculation patterns. These features were investigated during an ozone episode with hourly concentrations exceeding 200 μg m−3 that occurred on July 2015 in Naples (Italy), one of the largest and densest conurbations in the Mediterranean region. Aircraft measurements were taken at heights from 150 to 1500 m AGL and compared and integrated with high-resolution meteorological and air quality model simulations to investigate local circulation and pollutants dynamics. The integration of airborne measurements, surface observations, and modeling established a framework to assess the photochemical phenomena in the area. Sea breezes and local emissions triggered ozone production at inland areas, causing high concentrations between the coast and the Apennine chain. Ozone was then injected into the upper boundary layer and transported toward the sea by the wind rotation occurring above 500 m AGL, causing a complex vertical layering of concentrations, with maxima between 500 and 800 m AGL. Vertical growth of the ozone concentration profile was also caused by the decrease of the boundary layer depth occurring when the breeze front reached the inland area carrying NOx-rich air from the densely populated coast and favoring titration near the surface. Although the whole airshed was a net ozone producer, local surface concentrations were determined by a complex interaction of atmospheric flow and chemistry at different scales, supporting the need for coordinated efforts to control smog precursors over wide areas.

Oscillation of Surface PM2.5 Concentration Resulting from an Alternation of Easterly and Southerly Winds in Beijing: Mechanisms and Implications

We used simultaneous measurements of surface PM2.5 concentration and vertical profiles of aerosol concentration, temperature, and humidity, together with regional air quality model simulations, to study an episode of aerosol pollution in Beijing from 15 to 19 November 2016. The potential effects of easterly and southerly winds on the surface concentrations and vertical profiles of the PM2.5 pollution were investigated. Favorable easterly winds produced strong upward motion and were able to transport the PM2.5 pollution at the surface to the upper levels of the atmosphere. The amount of surface PM2.5 pollution transported by the easterly winds was determined by the strength and height of the upward motion produced by the easterly winds and the initial height of the upward wind. A greater amount of PM2.5 pollution was transported to upper levels of the atmosphere by upward winds with a lower initial height. The pollutants were diluted by easterly winds from clean ocean air masses. The inversion layer was destroyed by the easterly winds and the surface pollutants and warm air masses were then lifted to the upper levels of the atmosphere, where they re-established a multi-layer inversion. This region of inversion was strengthened by the southerly winds, increasing the severity of pollution. A vortex was produced by southerly winds that led to the convergence of air along the Taihang Mountains. Pollutants were transported from southern–central Hebei Province to Beijing in the boundary layer. Warm advection associated with the southerly winds intensified the inversion produced by the easterly winds and a more stable boundary layer was formed. The layer with high PM2.5 concentration became dee-per with persistent southerly winds of a certain depth. The polluted air masses then rose over the northern Taihang Mountains to the northern mountainous regions of Hebei Province.

Impacts of different characterizations of large-scale background on simulated regional-scale ozone over the continental United States.

This study analyzes simulated regional-scale ozone burdens both near the surface and aloft, estimates process contributions to these burdens, and calculates the sensitivity of the simulated regional-scale ozone burden to several key model inputs with a particular emphasis on boundary conditions derived from hemispheric or global-scale models. The Community Multiscale Air Quality (CMAQ) model simulations supporting this analysis were performed over the continental US for the year 2010 within the context of the Air Quality Model Evaluation International Initiative (AQMEII) and Task Force on Hemispheric Transport of Air Pollution (TF-HTAP) activities. CMAQ process analysis (PA) results highlight the dominant role of horizontal and vertical advection on the ozone burden in the mid-to-upper troposphere and lower stratosphere. Vertical mixing, including mixing by convective clouds, couples fluctuations in free-tropospheric ozone to ozone in lower layers. Hypothetical bounding scenarios were performed to quantify the effects of emissions, boundary conditions, and ozone dry deposition on the simulated ozone burden. Analysis of these simulations confirms that the characterization of ozone outside the regional-scale modeling domain can have a profound impact on simulated regional-scale ozone. This was further investigated by using data from four hemispheric or global modeling systems (Chemistry - Integrated Forecasting Model (C-IFS), CMAQ extended for hemispheric applications (H-CMAQ), the Goddard Earth Observing System model coupled to chemistry (GEOS-Chem), and AM3) to derive alternate boundary conditions for the regional-scale CMAQ simulations. The regional-scale CMAQ simulations using these four different boundary conditions showed that the largest ozone abundance in the upper layers was simulated when using boundary conditions from GEOS-Chem, followed by the simulations using C-IFS, AM3, and H-CMAQ boundary conditions, consistent with the analysis of the ozone fields from the global models along the CMAQ boundaries. Using boundary conditions from AM3 yielded higher springtime ozone columns burdens in the middle and lower troposphere compared to boundary conditions from the other models. For surface ozone, the differences between the AM3-driven CMAQ simulations and the CMAQ simulations driven by other large-scale models are especially pronounced during spring and winter where they can reach more than 10 ppb for seasonal mean ozone mixing ratios and as much as 15 ppb for domain-averaged daily maximum 8 h average ozone on individual days. In contrast, the differences between the C-IFS-, GEOS-Chem-, and H-CMAQ-driven regional-scale CMAQ simulations are typically smaller. Comparing simulated sur face ozone mixing ratios to observations and computing seasonal and regional model performance statistics revealed that boundary conditions can have a substantial impact on model performance. Further analysis showed that boundary conditions can affect model performance across the entire range of the observed distribution, although the impacts tend to be lower during summer and for the very highest observed percentiles. The results are discussed in the context of future model development and analysis opportunities.

Modeling the impact of chlorine emissions from coal combustion and prescribed waste incineration on tropospheric ozone formation in China

Abstract. Chlorine radicals can enhance atmospheric oxidation, which potentially increases tropospheric ozone concentration. However, few studies have been done to quantify the impact of chlorine emissions on ozone formation in China due to the lack of a chlorine emission inventory used in air quality models with sufficient resolution. In this study, the Anthropogenic Chlorine Emissions Inventory for China (ACEIC) was developed for the first time, including emissions of hydrogen chloride (HCl) and molecular chlorine (Cl2) from coal combustion and prescribed waste incineration (waste incineration plant). The HCl and Cl2 emissions from coal combustion in China in 2012 were estimated to be 232.9 and 9.4 Gg, respectively, while HCl emission from prescribed waste incineration was estimated to be 2.9 Gg. Spatially the highest emissions of HCl and Cl2 were found in the North China Plain, the Yangtze River Delta, and the Sichuan Basin. Air quality model simulations with the Community Multiscale Air Quality (CMAQ) modeling system were performed for November 2011, and the modeling results derived with and without chlorine emissions were compared. The magnitude of the simulated HCl, Cl2 and ClNO2 agreed reasonably with the observation when anthropogenic chlorine emissions were included in the model. The inclusion of the ACEIC increased the concentration of fine particulate Cl−, leading to enhanced heterogeneous reactions between Cl− and N2O5, which resulted in the higher production of ClNO2. Photolysis of ClNO2 and Cl2 in the morning and the reaction of HCl with OH in the afternoon produced chlorine radicals which accelerated tropospheric oxidation. When anthropogenic chlorine emissions were included in the model, the monthly mean concentrations of fine particulate Cl−, daily maximum 1 h ClNO2, and Cl radicals were estimated to increase by up to about 2.0 µg m−3, 773 pptv, and 1.5 × 103 molecule cm−3 in China, respectively. Meanwhile, the monthly mean daily maximum 8 h O3 concentration was found to increase by up to 2.0 ppbv (4.1 %), while the monthly mean NOx concentration decreased by up to 0.5 ppbv (6.1 %). The anthropogenic chlorine emissions potentially increased the 1 h O3 concentration by up to 7.7 ppbv in China. This study highlights the need for the inclusion of anthropogenic chlorine emission in air quality modeling and demonstrated its importance in tropospheric ozone formation.

The importance of spatial resolutions of Community Multiscale Air Quality (CMAQ) models on health impact assessment

We used the Community Multiscale Air Quality (CMAQ) simulation model to predict daily average of fine particulate matter (PM2.5) concentrations. The primary focus of our study was to investigate the sensitivity of CMAQ prediction accuracy associated with the horizontal grid resolutions and assess its impact on human health studies. To illustrate our point we ran CMAQ model at 4 km and 12 km resolutions over New York State for the year 2011, and systematically assessed the differences between two modeled PM2.5 concentrations. Model performance was evaluated against PM2.5 measured values at monitoring stations. The results indicated that simulations at both 4 km and 12 km resolutions reproduced measured PM2.5 values with fractional error (54.41% for 4 km and 52.28% for 12 km) that are within the recommend performance criteria except for summer seasons and rural areas. Additionally, model results at 12 km compared to 4 km resolution generally performed better and had substantially lower computational burden. In our health impact assessment study, we found that estimated adverse health outcomes associated with PM2.5 exposure derived from the two CMAQ models were compatible, especially in rural areas. Based on our findings, we conclude that the CMAQ simulation at 12 km resolution with further calibration and/or downscaling is a viable option than 4 km simulation to estimate small-scale within-city variations of air pollution concentrations.

Trajectories of Cognitive Aging in Older Women: Role of Fine Particulate Matter

Background/Aim: Cross-sectional studies have found associations of cognitive deficits with long-term exposure to particulate matter. Few longitudinal studies have shown inconsistent associations, and their analyses with limited neuropsychological assessments could not separate learning effects from trajectories of cognitive decline in late life. Methods: We examined how fine particulate matter (PM2.5: PM with aerodynamic diameters <2.5µm) was associated with cognitive declines in a geographically-diverse US population of older women (N=7064; aged 65-80) from the Women’s Health Initiative Memory Study, with up to 9 annual global cognitive assessments (in 6.3±2.1 visits) using the Modified Mini-Mental State (3MS) Exam. Given residential histories and ambient monitoring data coupled with air quality model simulation, the Bayesian Maximum Entropy method was used to estimate long-term (3-year average) PM2.5 exposures preceding each visit. We fit longitudinal linear mixed-effects models to standardized 3MS z-scores and assessed how cognitive trajectories were influenced by PM2.5 exposures, adjusting for socio-demographic (age; residential geography; race/ethnicity; education; income; employment status), lifestyle (smoking; alcohol use), and clinical factors (body mass index; hypertension; cardiovascular diseases). Results: Two concurrent processes of cognitive aging were identifiable. The first trajectory showed 3MS scores increased with visit numbers (p<0.001), likely reflecting a systematic difference that included the learning effect. The second trajectory was defined by the age-related decrease in 3MS, incrementally with faster declines during the extended follow-up (interaction p<0.0001). For the first trajectory, we found no appreciable differences by PM2.5 exposure (interaction p=0.88). The effect of aging on 3MS was accelerated by increased PM2.5 (interaction p<0.0001); this apparent neurotoxic effect was masked when the second trajectory was ignored in the mixed-effect model. Conclusions: This longitudinal study provides further evidence that late-life exposure to PM2.5 accelerates age-related cognitive decline. Our analyses also demonstrate the importance of accounting for the underlying trajectories of cognitive aging in community-dwelling populations.

The impact of marine shipping and its DECA control on air quality in the Pearl River Delta, China

Marine trade has significantly expanded over the past decades aiding to the economic development of the maritime countries, yet, this has been associated with a considerable increase in pollution emission from shipping operation. This study aims at considering both sides of the spectrum at the same time, which is including both public and shipping business. Of the key significance would be to optimize the operation of the shipping industry, such that its impact on air pollution is minimized, without, however, significant escalation of its cost, and therefore to protect the whole seaborne trade. To do this, we considered the impacts of three control strategies, including the current emission control area (ECA) design, as well two additional ones. Thus the first scenario (DECA1) was based on the China's domestic emission control area (DECA), which was set up in 2016. The DECA1 scale was only 12 nautical miles, which was much smaller than the emission control areas in US or Europe. We defined the second scenario (DECA2), by stretching the zone to 200 nautical miles towards the ocean, modeling it on the ECA in North America. The third scenario (DECA3), on the other hand, expanded the 12 nautical miles control zone along the whole coastline. To investigate the impact of shipping emissions on air quality, a shipping emission calculation model and an air quality simulation model were used, and Pearl River Delta (PRD), China was chosen to serve as a case study. The study demonstrated that in 2013 marine shipping emissions contributed on average 0.33 and 0.60μg·m−3, respectively to the land SO2 and PM2.5 concentrations in the PRD, and that the concentrations were high along the coastline. The DECA1 policy could effectively reduce SO2 and PM2.5 concentrations in the port regions, and the average reduction in the land area were 9.54% and 2.7%, respectively. Compared with DECA1, DECA2 would not measurably improve the air quality, while DECA3 would effectively decrease the pollution in the entire coast area. Thus, instead of expanding emission control area far to the ocean, it is more effective to control emissions along the coastline to secure the best air quality and lower the health impacts. By doing this, 19 million dollars of fuel cost could be saved per year. The saved cost could help the ship owners to endure, considering the current low profits of the seaborne trade, and thus to protect the overall growth of the economy.

A Comparison of Simulated and Field-Derived Leaf Area Index (LAI) and Canopy Height Values from Four Forest Complexes in the Southeastern USA.

Vegetative leaf area is a critical input to models that simulate human and ecosystem exposure to atmospheric pollutants. Leaf area index (LAI) can be measured in the field or numerically simulated, but all contain some inherent uncertainty that is passed to the exposure assessments that use them. LAI estimates for minimally managed or natural forest stands can be particularly difficult to develop as a result of interspecies competition, age and spatial distribution. Satellite-based LAI estimates hold promise for retrospective analyses, but we must continue to rely on numerical models for alternative management analysis. Our objective for this study is to calculate and validate LAI estimates generated from the USDA Environmental Policy Impact Climate (EPIC) model (a widely used, field-scale, biogeochemical model) on four forest complexes spanning three physiographic provinces in Virginia and North Carolina. Measurements of forest composition (species and number), LAI, tree diameter, basal area, and canopy height were recorded at each site during the 2002 field season. Calibrated EPIC results show stand-level temporally resolved LAI estimates with R2 values ranging from 0.69 to 0.96, and stand maximum height estimates within 20% of observation. This relatively high level of performance is attributable to EPIC’s approach to the characterization of forest stand biogeochemical budgets, stand history, interspecies competition and species-specific response to local weather conditions. We close by illustrating the extension of this site-level approach to scales that could support regional air quality model simulations.

A high-resolution and observationally constrained OMI NO<sub>2</sub> satellite retrieval

Abstract. This work presents a new high-resolution NO2 dataset derived from the NASA Ozone Monitoring Instrument (OMI) NO2 version 3.0 retrieval that can be used to estimate surface-level concentrations. The standard NASA product uses NO2 vertical profile shape factors from a 1.25° × 1° (∼ 110 km × 110 km) resolution Global Model Initiative (GMI) model simulation to calculate air mass factors, a critical value used to determine observed tropospheric NO2 vertical columns. To better estimate vertical profile shape factors, we use a high-resolution (1.33 km × 1.33 km) Community Multi-scale Air Quality (CMAQ) model simulation constrained by in situ aircraft observations to recalculate tropospheric air mass factors and tropospheric NO2 vertical columns during summertime in the eastern US. In this new product, OMI NO2 tropospheric columns increase by up to 160 % in city centers and decrease by 20–50 % in the rural areas outside of urban areas when compared to the operational NASA product. Our new product shows much better agreement with the Pandora NO2 and Airborne Compact Atmospheric Mapper (ACAM) NO2 spectrometer measurements acquired during the DISCOVER-AQ Maryland field campaign. Furthermore, the correlation between our satellite product and EPA NO2 monitors in urban areas has improved dramatically: r2 = 0.60 in the new product vs. r2 = 0.39 in the operational product, signifying that this new product is a better indicator of surface concentrations than the operational product. Our work emphasizes the need to use both high-resolution and high-fidelity models in order to recalculate satellite data in areas with large spatial heterogeneities in NOx emissions. Although the current work is focused on the eastern US, the methodology developed in this work can be applied to other world regions to produce high-quality region-specific NO2 satellite retrievals.

Observations of biomass burning tracers in PM2.5 at two megacities in North China during 2014 APEC summit

To evaluate the effectiveness of biomass burning control measures on PM2.5 reduction, day- and nighttime PM2.5 samples were collected at two urban sites in North China, one in Beijing (BJ) and the other in Shijiazhuang (SJZ), during the 2014 Asia-Pacific Economic Cooperation (APEC) summit. Typical biomass burning aerosol tracers including levoglucosan (LG), Mannosan (MN), and water-soluble potassium (K+), together with other water-soluble ions and carbonaceous species were determined. The levels of biomass burning tracers dropped dramatically during the APEC period when open biomass burning activities were well controlled in North China, yet they increased sharply to even higher levels during the post-APEC period. Distinct linear regression relationships between LG and MN were found with lower LG/MN ratios from periods with much reduced open biomass burning activities. This was likely resulted from the reduced open crop residues burning and increased residential wood burning emissions, as was also supported by the simultaneous decrease in K+/LG ratio. The positive matrix factorization and air quality model simulation analyses suggested that PM2.5 concentration produced from biomass burning sources was reduced by 22% at BJ and 46% at SJZ during the APEC period compared to pre-APEC period, although they increased to higher levels after APEC mainly due to increased residential biomass burning emissions in winter heating season. Biomass burning was also found to be the most important contributor to carbonaceous species that might cause significant light extinction in this region. This study not only suggested implementing biomass burning controls measures were helpful to reduce PM2.5 in North China, but also pointed out both open crop residues burning and indoor biomass burning activities could make substantial contributions to PM2.5 and its major components in urban areas in North China.

Improved provincial emission inventory and speciation profiles of anthropogenic non-methane volatile organic compounds: a case study for Jiangsu, China

Abstract. Non-methane volatile organic compounds (NMVOCs) are the key precursors of ozone (O3) and secondary organic aerosol (SOA) formation. Accurate estimation of their emissions plays a crucial role in air quality simulation and policy making. We developed a high-resolution anthropogenic NMVOC emission inventory for Jiangsu in eastern China from 2005 to 2014, based on detailed information of individual local sources and field measurements of source profiles of the chemical industry. A total of 56 NMVOCs samples were collected in nine chemical plants and were then analyzed with a gas chromatography – mass spectrometry system (GC-MS). Source profiles of stack emissions from synthetic rubber, acetate fiber, polyether, vinyl acetate and ethylene production, and those of fugitive emissions from ethylene, butanol and octanol, propylene epoxide, polyethylene and glycol production were obtained. Various manufacturing technologies and raw materials led to discrepancies in source profiles between our domestic field tests and foreign results for synthetic rubber and ethylene production. The provincial NMVOC emissions were calculated to increase from 1774 Gg in 2005 to 2507 Gg in 2014, and relatively large emission densities were found in cities along the Yangtze River with developed economies and industries. The estimates were larger than those from most other available inventories, due mainly to the complete inclusion of emission sources and to the elevated activity levels from plant-by-plant investigation in this work. Industrial processes and solvent use were the largest contributing sectors, and their emissions were estimated to increase, respectively, from 461 to 958 and from 38 to 966 Gg. Alkanes, aromatics and oxygenated VOCs (OVOCs) were the most important species, accounting for 25.9–29.9, 20.8–23.2 and 18.2–21.0 % to annual total emissions, respectively. Quantified with a Monte Carlo simulation, the uncertainties of annual NMVOC emissions vary slightly through the years, and the result for 2014 was −41 to +93 %, expressed as 95 % confidence intervals (CI). Reduced uncertainty was achieved compared to previous national and regional inventories, attributed partly to the detailed classification of emission sources and to the use of information at plant level in this work. Discrepancies in emission estimation were explored for the chemical and refinery sectors with various data sources and methods. Compared with the Multi-resolution Emission Inventory for China (MEIC), the spatial distribution of emissions in this work were more influenced by the locations of large point sources, and smaller emissions were found in urban area for developed cities in southern Jiangsu. In addition, discrepancies were found between this work and MEIC in the speciation of NMVOC emissions under the atmospheric chemistry mechanisms CB05 and SAPRC99. The difference in species OLE1 resulted mainly from the updated source profile of building paint use and the differences in other species from the varied sector contributions to emissions in the two inventories. The Community Multi-scale Air Quality (CMAQ) model simulation was applied to evaluate the two inventories, and better performance (indicated by daily 1 h maximum O3 concentrations in Nanjing) were found for January, April and October 2012 when the provincial inventory was used.

Application of WRF Model for Air Quality Modelling and AERMOD - A Survey

ABSTRACTMeteorology plays a crucial role in air quality. The presence of uncertainties of a significant nature in the meteorological profile used during air quality model simulation has the potential to affect negatively the results of the simulations. This paper describes a most recent version of the meteorological model called Weather Research and Forecasting (WRF) model and its importance in air quality. The performance of WRF depends upon the intended application and parameterization scheme of physics options. WRF model is also applied to investigate the simulation results with various land surface models (LSMs) and Planetary Boundary Layer (PBL) parameterizations and various set of microphysics options. It predicts various meteorological spatial parameters like mixing layer height, temperature, humidity, rain fall, cloud cover and wind. The WRF results are integrated with air quality model (AQM) and the AQM depends upon the performance of WRF. It has been applied for evaluation of national pollution control policy, behaviour of plume rise, property of aerosols, prediction of Ozone, SO2, NOx, PM10, PM2.5 etc. using AQM for various sources. The effect of topography and different seasons on the concentration of pollutants in the atmosphere has also been studied using AQM. AQM AERMOD has also been reviewed with various other AQM models such as ADMS-Urban and CALPUFF. AERMOD has been used for different time scales, health risk assessment, evaluation of various control strategies, Environmental Impact Assessment (EIA) studies and emission factor estimation. This paper presents the importance of meteorological model to AQM as well as many applications of AQM to demonstrate various scientific questions and policies.

An Integrated Air Quality Model and Optimization Model for Regional Economic and Environmental Development: A Case Study of Tangshan, China

ABSTRACTAn interval programming optimization model was formulated to develop effective and feasible regional economic structure adjustment plan using Tangshan Municipality in China as a case study. The optimization model was coupled with a WRF-CAMx-PSAT air quality simulation system through the estimated industrial emission sensitivity coefficients and equivalent coefficients for PM2.5 concentrations. Seven categories of industries were examined, and the results indicated that industries with higher emission sensitivity coefficients should be given priority for control. The effectiveness of the obtained optimal schemes was further assessed by the air quality simulation system. It indicated that PM2.5 concentrations in Tangshan would decrease by [33.5%, 39.3%] than those in 2013. This study provided an effective method framework for industries to maximize profits while meeting certain air quality constraints under uncertainty through the coupling of air quality simulation and optimization models.

Application of Air Quality Simulation Models: Perspectives from a Policymaker

Application of Air Quality Simulation Models: Perspectives from a Policymaker

Evaluation of atmospheric nitrogen deposition model performance in the context of U.S. critical load assessments

Air quality models are widely used to estimate pollutant deposition rates and thereby calculate critical loads and critical load exceedances (model deposition > critical load). However, model operational performance is not always quantified specifically to inform these applications. We developed a performance assessment approach designed to inform critical load and exceedance calculations, and applied it to the Pacific Northwest region of the U.S. We quantified wet inorganic N deposition performance of several widely-used air quality models, including five different Community Multiscale Air Quality Model (CMAQ) simulations, the Tdep model, and ‘PRISM x NTN’ model. Modeled wet inorganic N deposition estimates were compared to wet inorganic N deposition measurements at 16 National Trends Network (NTN) monitoring sites, and to annual bulk inorganic N deposition measurements at Mount Rainier National Park. Model bias (model – observed) and error (|model – observed|) were expressed as a percentage of regional critical load values for diatoms and lichens. This novel approach demonstrated that wet inorganic N deposition bias in the Pacific Northwest approached or exceeded 100% of regional diatom and lichen critical load values at several individual monitoring sites, and approached or exceeded 50% of critical loads when averaged regionally. Even models that adjusted deposition estimates based on deposition measurements to reduce bias or that spatially-interpolated measurement data, had bias that approached or exceeded critical loads at some locations. While wet inorganic N deposition model bias is only one source of uncertainty that can affect critical load and exceedance calculations, results demonstrate expressing bias as a percentage of critical loads at a spatial scale consistent with calculations may be a useful exercise for those performing calculations. It may help decide if model performance is adequate for a particular calculation, help assess confidence in calculation results, and highlight cases where a non-deterministic approach may be needed.

Response surface modeling-based source contribution analysis and VOC emission control policy assessment in a typical ozone-polluted urban Shunde, China

To develop a sound ozone (O3) pollution control strategy, it is important to well understand and characterize the source contribution due to the complex chemical and physical formation processes of O3. Using the “Shunde” city as a pilot summer case study, we apply an innovative response surface modeling (RSM) methodology based on the Community Multi-Scale Air Quality (CMAQ) modeling simulations to identify the O3 regime and provide dynamic analysis of the precursor contributions to effectively assess the O3 impacts of volatile organic compound (VOC) control strategy. Our results show that Shunde is a typical VOC-limited urban O3 polluted city. The “Jiangmen” city, as the main upper wind area during July 2014, its VOCs and nitrogen oxides (NOx) emissions make up the largest contribution (9.06%). On the contrary, the contribution from local (Shunde) emission is lowest (6.35%) among the seven neighbor regions. The local VOCs industrial source emission has the largest contribution comparing to other precursor emission sectors in Shunde. The results of dynamic source contribution analysis further show that the local NOx control could slightly increase the ground O3 under low (10.00%) and medium (40.00%) reduction ratios, while it could start to turn positive to decrease ground O3 under the high NOx abatement ratio (75.00%). The real-time assessment of O3 impacts from VOCs control strategies in Pearl River Delta (PRD) shows that the joint regional VOCs emission control policy will effectively reduce the ground O3 concentration in Shunde.

Evaluation of improved land use and canopy representation in BEIS v3.61 with biogenic VOC measurements in California

Abstract. Biogenic volatile organic compounds (BVOC) participate in reactions that can lead to secondarily formed ozone and particulate matter (PM) impacting air quality and climate. BVOC emissions are important inputs to chemical transport models applied on local to global scales but considerable uncertainty remains in the representation of canopy parameterizations and emission algorithms from different vegetation species. The Biogenic Emission Inventory System (BEIS) has been used to support both scientific and regulatory model assessments for ozone and PM. Here we describe a new version of BEIS which includes updated input vegetation data and canopy model formulation for estimating leaf temperature and vegetation data on estimated BVOC. The Biogenic Emission Landuse Database (BELD) was revised to incorporate land use data from the Moderate Resolution Imaging Spectroradiometer (MODIS) land product and 2006 National Land Cover Database (NLCD) land coverage. Vegetation species data are based on the US Forest Service (USFS) Forest Inventory and Analysis (FIA) version 5.1 for 2002–2013 and US Department of Agriculture (USDA) 2007 census of agriculture data. This update results in generally higher BVOC emissions throughout California compared with the previous version of BEIS. Baseline and updated BVOC emission estimates are used in Community Multiscale Air Quality (CMAQ) Model simulations with 4 km grid resolution and evaluated with measurements of isoprene and monoterpenes taken during multiple field campaigns in northern California. The updated canopy model coupled with improved land use and vegetation representation resulted in better agreement between CMAQ isoprene and monoterpene estimates compared with these observations.

Understanding sources of organic aerosol during CalNex-2010 using the CMAQ-VBS

Abstract. Community Multiscale Air Quality (CMAQ) model simulations utilizing the traditional organic aerosol (OA) treatment (CMAQ-AE6) and a volatility basis set (VBS) treatment for OA (CMAQ-VBS) were evaluated against measurements collected at routine monitoring networks (Chemical Speciation Network (CSN) and Interagency Monitoring of Protected Visual Environments (IMPROVE)) and those collected during the 2010 California at the Nexus of Air Quality and Climate Change (CalNex) field campaign to examine important sources of OA in southern California. Traditionally, CMAQ treats primary organic aerosol (POA) as nonvolatile and uses a two-product framework to represent secondary organic aerosol (SOA) formation. CMAQ-VBS instead treats POA as semivolatile and lumps OA using volatility bins spaced an order of magnitude apart. The CMAQ-VBS approach underpredicted organic carbon (OC) at IMPROVE and CSN sites to a greater degree than CMAQ-AE6 due to the semivolatile POA treatment. However, comparisons to aerosol mass spectrometer (AMS) measurements collected at Pasadena, CA, indicated that CMAQ-VBS better represented the diurnal profile and primary/secondary split of OA. CMAQ-VBS SOA underpredicted the average measured AMS oxygenated organic aerosol (OOA, a surrogate for SOA) concentration by a factor of 5.2, representing a considerable improvement to CMAQ-AE6 SOA predictions (factor of 24 lower than AMS). We use two new methods, one based on species ratios (SOA/ΔCO and SOA/Ox) and another on a simplified SOA parameterization, to apportion the SOA underprediction for CMAQ-VBS to slow photochemical oxidation (estimated as 1.5 × lower than observed at Pasadena using −log(NOx : NOy)), low intrinsic SOA formation efficiency (low by 1.6 to 2 × for Pasadena), and low emissions or excessive dispersion for the Pasadena site (estimated to be 1.6 to 2.3 × too low/excessive). The first and third factors are common to CMAQ-AE6, while the intrinsic SOA formation efficiency for that model is estimated to be too low by about 7 × . From source-apportioned model results, we found most of the CMAQ-VBS modeled POA at the Pasadena CalNex site was attributable to meat cooking emissions (48 %, consistent with a substantial fraction of cooking OA in the observations). This is compared to 18 % from gasoline vehicle emissions, 13 % from biomass burning (in the form of residential wood combustion), and 8 % from diesel vehicle emissions. All "other" inventoried emission sources (e.g., industrial, point, and area sources) comprised the final 13 %. The CMAQ-VBS semivolatile POA treatment underpredicted AMS hydrocarbon-like OA (HOA) + cooking-influenced OA (CIOA) at Pasadena by a factor of 1.8 compared to a factor of 1.4 overprediction of POA in CMAQ-AE6, but it did capture the AMS diurnal profile of HOA and CIOA well, with the exception of the midday peak. Overall, the CMAQ-VBS with its semivolatile treatment of POA, SOA from intermediate volatility organic compounds (IVOCs), and aging of SOA improves SOA model performance (though SOA formation efficiency is still 1.6–2 × too low). However, continued efforts are needed to better understand assumptions in the parameterization (e.g., SOA aging) and provide additional certainty to how best to apply existing emission inventories in a framework that treats POA as semivolatile, which currently degrades existing model performance at routine monitoring networks. The VBS and other approaches (e.g., AE6) require additional work to appropriately incorporate IVOC emissions and subsequent SOA formation.

Air Pollution and Preterm Birth in the U.S. State of Georgia (2002-2006): Associations with Concentrations of 11 Ambient Air Pollutants Estimated by Combining Community Multiscale Air Quality Model (CMAQ) Simulations with Stationary Monitor Measurements.

Background:Previous epidemiologic studies suggest associations between preterm birth and ambient air pollution.Objective:We investigated associations between 11 ambient air pollutants, estimated by combining Community Multiscale Air Quality model (CMAQ) simulations with measurements from stationary monitors, and risk of preterm birth (< 37 weeks of gestation) in the U.S. state of Georgia.Methods:Birth records for singleton births ≥ 27 weeks of gestation with complete covariate information and estimated dates of conception between 1 January 2002 and 28 February 2006 were obtained from the Office of Health Indicators for Planning, Georgia Department of Public Health (n = 511,658 births). Daily pollutant concentrations at 12-km resolution were estimated for 11 ambient air pollutants. We used logistic regression with county-level fixed effects to estimate associations between preterm birth and average pollutant concentrations during the first and second trimester. Discrete-time survival models were used to estimate third-trimester and total pregnancy associations. Effect modification was investigated by maternal education, race, census tract poverty level, and county-level urbanicity.Results:Trimester-specific and total pregnancy associations (p < 0.05) were observed for several pollutants. All the traffic-related pollutants (carbon monoxide, nitrogen dioxide, PM2.5 elemental carbon) were associated with preterm birth [e.g., odds ratios for interquartile range increases in carbon monoxide during the first, second, and third trimesters and total pregnancy were 1.005 (95% CI: 1.001, 1.009), 1.007 (95% CI: 1.002, 1.011), 1.010 (95% CI: 1.006, 1.014), and 1.011 (95% CI: 1.006, 1.017)]. Associations tended to be higher for mothers with low educational attainment and African American mothers.Conclusion:Several ambient air pollutants were associated with preterm birth; associations were observed in all exposure windows.Citation:Hao H, Chang HH, Holmes HA, Mulholland JA, Klein M, Darrow LA, Strickland MJ. 2016. Air pollution and preterm birth in the U.S. state of Georgia (2002–2006): associations with concentrations of 11 ambient air pollutants estimated by combining Community Multiscale Air Quality Model (CMAQ) simulations with stationary monitor measurements. Environ Health Perspect 124:875–880; http://dx.doi.org/10.1289/ehp.1409651

Long-term particulate matter modeling for health effect studies in California – Part 1: Model performance on temporal and spatial variations

Abstract. For the first time, a ~ decadal (9 years from 2000 to 2008) air quality model simulation with 4 km horizontal resolution over populated regions and daily time resolution has been conducted for California to provide air quality data for health effect studies. Model predictions are compared to measurements to evaluate the accuracy of the simulation with an emphasis on spatial and temporal variations that could be used in epidemiology studies. Better model performance is found at longer averaging times, suggesting that model results with averaging times ≥ 1 month should be the first to be considered in epidemiological studies. The UCD/CIT model predicts spatial and temporal variations in the concentrations of O3, PM2.5, elemental carbon (EC), organic carbon (OC), nitrate, and ammonium that meet standard modeling performance criteria when compared to monthly-averaged measurements. Predicted sulfate concentrations do not meet target performance metrics due to missing sulfur sources in the emissions. Predicted seasonal and annual variations of PM2.5, EC, OC, nitrate, and ammonium have mean fractional biases that meet the model performance criteria in 95, 100, 71, 73, and 92% of the simulated months, respectively. The base data set provides an improvement for predicted population exposure to PM concentrations in California compared to exposures estimated by central site monitors operated 1 day out of every 3 days at a few urban locations. Uncertainties in the model predictions arise from several issues. Incomplete understanding of secondary organic aerosol formation mechanisms leads to OC bias in the model results in summertime but does not affect OC predictions in winter when concentrations are typically highest. The CO and NO (species dominated by mobile emissions) results reveal temporal and spatial uncertainties associated with the mobile emissions generated by the EMFAC 2007 model. The WRF model tends to overpredict wind speed during stagnation events, leading to underpredictions of high PM concentrations, usually in winter months. The WRF model also generally underpredicts relative humidity, resulting in less particulate nitrate formation, especially during winter months. These limitations must be recognized when using data in health studies. All model results included in the current manuscript can be downloaded free of charge at http://faculty.engineering.ucdavis.edu/kleeman/ .