Sparse Matrix Operator Kernel Emissions Research Articles

Assessing the roles emission sources and atmospheric processes play in simulating <i>δ</i><sup>15</sup>N of atmospheric NO<sub><i>x</i></sub> and NO<sub>3</sub><sup>−</sup> using CMAQ (version 5.2.1) and SMOKE (version 4.6)

Abstract. Nitrogen oxides (NOx= nitric oxide (NO) + nitrogen dioxide (NO2)) are important trace gases that affect atmospheric chemistry, air quality, and climate. Contemporary development of NOx emissions inventories is limited by the understanding of the roles of vegetation (net NOx source or net sink), vehicle emissions from gasoline- and diesel-powered vehicles, the application of NOx emission control technologies, and accurate verification techniques. The nitrogen stable isotope composition (δ15N) of NOx is an effective tool to evaluate the accuracy of the NOx emission inventories, which are based on different assumptions. In this study, we traced the changes in δ15N values of NOx along the “journey” of atmospheric NOx, driven by atmospheric processes after different sources emit NOx into the atmosphere. The 15N was incorporated into the emission input dataset, generated from the US EPA trace gas emission model SMOKE (Sparse Matrix Operator Kernel Emissions). Then the 15N-incorporated emission input dataset was used to run the CMAQ (Community Multiscale Air Quality) modeling system. By enhancing NOx deposition, we simulated the expected δ15N of NO3-, assuming no isotope fractionation during chemical conversion or deposition. The simulated spatiotemporal patterns in NOx isotopic composition for both SMOKE outputs (simulations under the “emission only” scenario) and CMAQ outputs (simulations under the “emission + transport + enhanced NOx loss” scenario) were compared with corresponding measurements in West Lafayette, Indiana, USA. The simulations under the emission + transport + enhanced NOx loss scenario were also compared to δ15N of NO3- at NADP (National Atmospheric Deposition Program) sites. The results indicate the potential underestimation of emissions from soil, livestock waste, off-road vehicles, and natural-gas power plants and the potential overestimation of emissions from on-road vehicles and coal-fired power plants, if only considering the difference in NOx isotopic composition for different emission sources. After considering the mixing, dispersion, transport, and deposition of NOx emission from different sources, the estimation of atmospheric δ15N(NOx) shows better agreement (by ∼ 3 ‰) with observations.

Influence of the Grid Resolutions on the Computer-Simulated Surface Air Pollution Concentrations in Bulgaria

The present study aims to demonstrate the effects of horizontal grid resolution on the simulated pollution concentration fields over Bulgaria. The computer simulations are performed with a set of models used worldwide—the Weather Research and Forecasting Model (WRF)—the meteorological preprocessor, the Community Multiscale Air Quality Modeling System (CMAQ)—chemical transport model, Sparse Matrix Operator Kernel Emissions (SMOKE)—emission model. The large-scale (background) meteorological data used in the study were taken from the ‘NCEP Global Analysis Data’ with a horizontal resolution of 1° × 1°. Using the ‘nesting’ capabilities of the WRF and CMAQ models, a resolution of 9 km was achieved for the territory of Bulgaria by sequentially solving the task in several consecutive nested areas. Three cases are considered in this paper: Case 1: The computer simulations result from the domain with a horizontal resolution (both of the emission source description and the grid) of 27 km.; Case 2: The computer simulations result from the domain with a horizontal resolution (both of the emission source description and the grid) of 9 km.; Case 3: A hybrid case with the computer simulations performed with a grid resolution of 9 km, but with emissions such as in the 27 km × 27 km domain. The simulations were performed, for all the three cases, for the period 2007–2014 year, thus creating an ensemble large and comprehensive enough to reflect the most typical atmospheric conditions with their typical recurrence. The numerical experiments showed the significant impact of the grid resolution not only in the pollution concentration pattern but also in the demonstrated generalized characteristics. Averaged over a large territory (Bulgaria); however, the performances for cases one and two are quite similar. Bulgaria is a country with a complex topography and with several considerably large point sources. Thus, some of the conclusions made, though based on Bulgarian-specific experiments, may be of general interest.

Source apportionment modelling of PM2.5 using CMAQ-ISAM over a tropical coastal-urban area

This study aims to explain the role of local emission sources to PM2.5 mass concentration in a tropical coastal-urban area, highly influenced by industrial and urban emissions, located in the Southeast of Brazil. The Integrated Source Apportionment Method (ISAM) tool was coupled with the chemistry and transport Community Multiscale Air Quality (CMAQ) model (CMAQ-ISAM) to quantify the contribution of ten emission sectors of PM2.5. The simulations were performed over five months between spring 2019 and summer 2020 using a local inventory, which was processed by the Sparse Matrix Operator Kernel Emission (SMOKE). The meteorological fields were provided by the Weather Research and Forecasting (WRF-Urban) model. The boundary and initial conditions to the CMAQ-ISAM were performed by the GEOS-Chem model. The simulations results show that the road dust resuspension (36%) and point (17%) emissions sources were the major contributors to PM2.5 mass in the Metropolitan Region of Vitória (MRV). The boundary conditions (BCON), representing the transport contribution from sources outside the domain, were also a dominant contributor in the MRV (20% on average). Furthermore, the primary atmospheric pollutants emitted by the point (14%) and shipping (7%) sectors in the MRV also affected the cities located in the south region of the domain, strengthened by the wind fields that mostly come from the northeast direction.

Establishment of PM10 and PM2.5 emission inventories from wind erosion source and simulation of its environmental impact based on WEPS-Models3 in southern Xinjiang, China

Southern Xinjiang located in the far northwest of China is experiencing serious particulate matter (PM) pollution. The wind erosion has been recognized as a great contributor of PM10 and PM2.5 concentrations in southern Xinjiang. In this study we developed a method that used the Wind Erosion Prediction System (WEPS) to establish the PM10 and PM2.5 emission inventories from the wind erosion source in southern Xinjiang with a 10 km × 10 km spatial resolution and a temporal resolution of one month. The PM10 and PM2.5 emission inventories from wind erosion sources were provided as input to the Models3/SMOKE (Sparse Matrix Operator Kernel Emission). The Community Multi-scale Air Quality (CMAQ) model was employed to simulate PM10 and PM2.5 concentrations from wind erosion source and the results were compared with the monitoring data to verify the emission inventories. The total PM10 and PM2.5 emissions were 12 × 106 t and 4 × 106 t, respectively, and the emission per unit area were 14.6 t/km2 and 4.9 t/km2, respectively, in southern Xinjiang in 2016. The PM10 and PM2.5 emissions per unit area were highest in Kashi being 19.1 t/km2 and 9.1 t/km2, respectively. The total emissions peaked at 5.4 × 106 t/km2 in PM10 and 1.4 × 106 t/km2 in PM2.5 in Bazhou. The PM10 and PM2.5 emissions were highest in spring, followed by summer, winter and autumn. The Normalized Mean Error (NME) between the predicted monthly average PM10 and PM2.5 concentrations from wind erosion source and the results from multiplying the monitoring data by the source apportionment results of PM10 and PM2.5 were 25.3% and 26.1%, respectively, which indicated that the accuracies of the PM10 and PM2.5 emission inventories from wind erosion source in southern Xinjiang were satisfactory.

Expansion of a size disaggregation profile library for particulate matter emissions processing from three generic profiles to 36 source-type-specific profiles

ABSTRACT This study describes a significant upgrade to the particulate matter (PM) size disaggregation profile library used for preparing emissions files for the GEM-MACH (Global Environmental Multiscale-Modelling Air-quality and CHemistry) chemical transport model (CTM). This model uses a sectional (bin) approach to represent the PM size distribution, where one configuration employs 12 size bins to disaggregate PM2.5 and PM10 inventory emissions into the first 10 bins ranging from 0.01 to 10.24 μm in diameter. For the size disaggregation step, a small library of three generic PM size disaggregation profiles is currently applied for three broad source categories (area, mobile, and point). However, as might be expected, these generic profiles are not always representative: for example, emissions from two very different area sources ‒ paved road dust and residential wood combustion ‒ are disaggregated using the same generic size distribution profile. In order to improve the current small PM size disaggregation profile library, a comprehensive literature review was conducted: over 100 relevant publications were identified and PM size distribution profiles for 36 different emission source types were selected and compiled. These 36 source-type-specific PM size distribution profiles were then combined based on process type with corresponding PM speciation profiles to create a library of chemically speciated and size-resolved PM disaggregation profiles. This library can now be used by the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system for the 12-bin version of GEM-MACH to perform PM chemical speciation and size allocation in one step. The size-profile data collected and compiled in this study may also be used for emissions processing for other CTMs with a size-resolved representation of PM. Details of the compilation of the 36 PM size disaggregation profiles are discussed, and the differences in processed PM emissions based on the current and updated PM size disaggregation profile libraries are shown. Implications: A new and expanded particulate matter (PM) size disaggregation profile library covering 36 emission source types has been developed based on an extensive literature review. Its use can produce significant changes in the size allocation of bulk PM inventory emissions processed for input to size-resolved PM chemical transport models. Such models are used to predict atmospheric visibility and to simulate the interactions of aerosol particles with atmospheric radiation and with clouds. The use of more accurate, size-resolved primary PM emissions by these models should improve their predictive skill for atmospheric PM processes affecting air quality, meteorology, and climate.

Black and organic carbon fractions in fine particulate matter by sectors in the South Hemisphere emissions for decision-making on climate change and health effects.

Some databases report global emissions of certain pollutants. Emissions Database for Global Atmospheric Research (EDGAR) project is one of these, which also records emissions by sources. In this study, the emissions of black and organic carbon and fine particulate matter from the EDGAR database were used as an input to process it in the Sparse Matrix Operator Kernel Emissions (SMOKE) model. We showed the spatial distribution of the fraction of black and organic carbon in particulate matter from each source in the Southern Hemisphere. Also, we extracted these ratios for several cities in the domain of analysis. The results and methodology of this study could improve the emission inventories with bottom-up methodology in areas without information located at Southern Hemisphere. Also, it could be relevant to obtain better performance in air quality modeling at the local level for decision-making on climate change and health effects.

Synoptic condition-driven summertime ozone formation regime in Shanghai and the implication for dynamic ozone control strategies

Regarding the continuous worsening of tropospheric ozone pollution, the scenario in Shanghai is a microcosm of the entire China. Understanding the ozone formation regimes (OFRs), their variations, and driving factors is a prerequisite for formulating effective ozone control strategies. Traditional OFR estimation by numerical model, which often involves sensitivity analysis on at least tens of scenarios, is labor-intensive and time-consuming; therefore, it is not appropriate to make OFR forecasts to guide ozone contingency control. In this study, by using a localized modeling system consisting of the Weather Research and Forecasting, Sparse Matrix Operator Kernel Emissions, and Community Multiscale Air Quality models and considering the latest emission inventory over the Yangtze River Delta of China, we discovered a strong connection between the variations of large-scale circulation (LSC) and OFRs over Shanghai in July 2017, thereby providing an alternative way to infer OFR. During the northward movement of Western Pacific Subtropical High from South China Sea, the wind field over Shanghai changed from weak westerly to moderate southwesterly and to one without a distinct direction. The local OFR shifted from anthropogenic volatile organic compounds (AVOCs)-limited to NOx-limited and ultimately to the transitional regime. Such a variation in OFR is essentially driven by the spatial heterogeneity of NOx and AVOC emissions in different directions of Shanghai, brought on by the wind under different LSC patterns. With the existing weather forecasting technology, the LSC patterns can be well-predicted 48–72 h in advance. Hence, we propose the adoption of a dynamic ozone control strategy for Shanghai with the priority control target on AVOC or NOx emission sources adjusted according to the LSC pattern and OFR forecasts in a forthcoming O3 pollution episode. This would serve to maximize the peak ozone reduction under varying pollution conditions.

Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign

Abstract. Multiple observation data sets – Interagency Monitoring of Protected Visual Environments (IMPROVE) network data, the Automated Smoke Detection and Tracking Algorithm (ASDTA), Hazard Mapping System (HMS) smoke plume shapefiles and aircraft acetonitrile (CH3CN) measurements from the NOAA Southeast Nexus (SENEX) field campaign – are used to evaluate the HMS–BlueSky–SMOKE (Sparse Matrix Operator Kernel Emission)–CMAQ (Community Multi-scale Air Quality Model) fire emissions and smoke plume prediction system. A similar configuration is used in the US National Air Quality Forecasting Capability (NAQFC). The system was found to capture most of the observed fire signals. Usage of HMS-detected fire hotspots and smoke plume information was valuable for deriving both fire emissions and forecast evaluation. This study also identified that the operational NAQFC did not include fire contributions through lateral boundary conditions, resulting in significant simulation uncertainties. In this study we focused both on system evaluation and evaluation methods. We discussed how to use observational data correctly to retrieve fire signals and synergistically use multiple data sets. We also addressed the limitations of each of the observation data sets and evaluation methods.

Simulation of atmospheric mercury dispersion and deposition in Tehran city

In this study, dispersion and deposition of atmospheric mercury (Hg) in Tehran city was simulated using WRF-SMOKE-CMAQ models. The Weather Research and Forecasting (WRF) model was used to simulate the meteorological parameters. For validation of WRF results, the simulated wind speeds and temperatures were compared with the parameters measured at a meteorological station in Tehran city for 11 days (8 days in fall and 3 days in winter) in 2010–2011. The correlation coefficient (r) for temperature and wind speed was 0.94 and 0.49, respectively, indicating there was good agreement between measured and modeled results. An atmospheric mercury emission inventory was developed using the United Nations Environment Programme (UNEP), the United States Environmental Protection Agency AP-42 (US-EPA AP-42), and related papers. Sparse Matrix Operator Kernel Emissions (SMOKE) was used to allocate the atmospheric mercury emissions to the modeling domain, and the Community Multiscale Air Quality (CMAQ) model was used to simulate the concentration and deposition of atmospheric mercury. To validate the results of the CMAQ model, the simulated atmospheric particulate mercury (PHg) concentrations for 11 days were compared with the measured results at two different stations (Bagh Ferdows and Bahman Square) where it was measured by the Tehran Air Quality Control Company (AQCC). Comparison between the results from the modeled and measurements of PHg in fall was better than winter. Concentrations and dry depositions of the various forms of atmospheric mercury were higher in areas closer to mercury stationary emission sources.

A Multiscale Tiered Approach to Quantify Contributions: A Case Study of PM2.5 in South Korea During 2010–2017

We estimated long-term foreign contributions to the particulate matter of 2.5 μm or less in diameter (PM2.5) concentrations in South Korea with a set of air quality simulations. The Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Community Multiscale Air Quality (CMAQ) modeling system was used to simulate the base and sensitivity case after a 50% reduction of foreign emissions. The effects of horizontal modeling grid resolutions (27- and 9-km) was also investigated. For this study, we chose PM2.5 in South Korea during 2010–2017 for the case study and emissions from China as a representative foreign source. The 9-km simulation results show that the 8-year average contribution of the Chinese emissions in 17 provinces ranged from 40–65%, which is ~4% lower than that from the 27-km simulation for the high-tier government segments (particularly prominent in coastal areas). However, for the same comparison for low-tier government segments (i.e., 250 prefectures), the 9-km simulation presented lowered the foreign contribution by up to 10% compared to that from the 27-km simulation. Based on our study results, we recommend using high-resolution modeling results for regional contribution analyses to develop an air quality action plan as the receptor coverage decreases.

Development of a PM2.5 Forecasting System Integrating Low-cost Sensors for Ho Chi Minh City, Vietnam

ABSTRACT Air pollution is a serious concern in urban areas, especially cities such as Ho Chi Minh City (HCMC). Because the air quality directly affects people’s health, air quality monitoring is urgently needed. In this study, the models of Weather Research and Forecasting (WRF), Sparse Matrix Operator Kernel Emission (SMOKE), and Community Multiscale Air Quality (CMAQ) were integrated to develop an air quality forecasting system. Drawing input data from transportation and industrial emission inventories, the forecasting system was calibrated and configured using local parameters to deliver hourly forecasts for HCMC. To increase the accuracy of WRF and the meteorological forecasting, the global DEM and land use data were replaced by Lidar data, and land use data were also retrieved from MODIS. Output from the MOZART model served as the boundary conditions for CMAQ, and AOD values reported by the MODIS Aerosol Product were assimilated to enhance the accuracy of the results. A low-cost PM2.5 sensor connected to a LinkIt ONE, a development board for Internet of things (IoT) devices, was employed for calibration and verification. The strong correlation (R2 = 0.8) between the measured and predicted concentrations indicates that the estimates delivered by the proposed forecasting system are consistent with the values obtained via monitoring.

WRF-SMOKE-CMAQ modeling system for air quality evaluation in São Paulo megacity with a 2008 experimental campaign data.

Atmospheric pollutants are strongly affected by transport processes and chemical transformations that alter their composition and the level of contamination in a region. In the last decade, several studies have employed numerical modeling to analyze atmospheric pollutants. The objective of this study is to evaluate the performance of the WRF-SMOKE-CMAQ modeling system to represent meteorological and air quality conditions over São Paulo, Brazil, where vehicular emissions are the primary contributors to air pollution. Meteorological fields were modeled using the Weather Research and Forecasting model (WRF), for a 12-day period during the winter of 2008 (Aug. 10th-Aug. 22nd), using three nested domains with 27-km, 9-km, and 3-km grid resolutions, which covered the most polluted cities in São Paulo state. The 3-km domain was aligned with the Sparse Matrix Operator Kernel Emissions (SMOKE), which processes the emission inventory for the Models-3 Community Multiscale Air Quality Modeling System (CMAQ). Data from an aerosol sampling campaign was used to evaluate the modeling. The PM10 and ozone average concentration of the entire period was well represented, with correlation coefficients for PM10, varying from 0.09 in Pinheiros to 0.69 in ICB/USP, while for ozone, the correlation coefficients varied from 0.56 in Pinheiros to 0.67 in IPEN. However, the model underestimated the concentrations of PM2.5 during the experiment, but with ammonium showing small differences between predicted and observed concentrations. As the meteorological model WRF underestimated the rainfall and overestimated the wind speed, the accuracy of the air quality model was expected to be below the desired value. However, in general, the CMAQ model reproduced the behavior of atmospheric aerosol and ozone in the urban area of São Paulo.

Estimating population exposure to ambient polycyclic aromatic hydrocarbon in the United States – Part I: Model development and evaluation

PAHs (polycyclic aromatic hydrocarbons) in the environment are of significant concern due to their negative impact on human health. PAH measurements at the air toxics monitoring network stations alone are not sufficient to provide a complete picture of ambient PAH levels or to allow accurate assessment of public exposure in the United States. In this study, speciation profiles for PAHs were prepared using data assembled from existing emission profile data bases, and the Sparse Matrix Operator Kernel Emissions (SMOKE) model was used to generate the gridded national emissions of 16 priority PAHs in the US. The estimated emissions were applied to simulate ambient concentration of PAHs for January, April, July and October 2011, using a modified Community Multiscale Air Quality (CMAQ) model (v5.0.1) that treats the gas and particle phase partitioning of PAHs and their reactions in the gas phase and on particle surface. Predicted daily PAH concentrations at 61 air toxics monitoring sites generally agreed with observations, and averaging the predictions over a month reduced the overall error. The best model performance was obtained at rural sites, with an average mean fractional bias (MFB) of −0.03 and mean fractional error (MFE) of 0.70. Concentrations at suburban and urban sites were underestimated with overall MFB=−0.57 and MFE=0.89. Predicted PAH concentrations were highest in January with better model performance (MFB=0.12, MFE=0.69; including all sites), and lowest in July with worse model performance (MFB=−0.90, MFE=1.08). Including heterogeneous reactions of several PAHs with O3 on particle surface reduced the over-prediction bias in winter, although significant uncertainties were expected due to relative simple treatment of the heterogeneous reactions in the current model.

Source Apportionment of Sulfate and Nitrate over the Pearl River Delta Region in China

In this work, the Weather Research Forecast (WRF)–Sparse Matrix Operator Kernel Emission (SMOKE)–Comprehensive Air Quality Model with Extensions (CAMx) modeling system with particulate source apportionment technology (PSAT) module was used to study and analyze the source apportionment of sulfate and nitrate particulate matter in the Pearl River Delta region (PRD). The results show that superregional transport was an important contributor for both sulfates and nitrates in all 10 cities in this region in both February (winter) and August (summer). Especially in February, the average super-regional contribution of sulfate and nitrate reached up to 80% and 56% respectively. For the local and regional source category, power plant emissions (coal-fired and oil-fired) and industry emissions were important for sulfate formation in this region. Industry emissions and mobile emissions are important for nitrate formation in this region. In August, the sum of these two sources contributed around over 60% of local and regional nitrate. The contributions from power plant emissions and marine emissions became important in August due to the southerly prevailing wind direction. Area sources and biogenic emissions were negligible for sulfate and nitrate formation in this region. Our results reveal that cross-province cooperation is necessary for control of sulfates and nitrates in this region.

Human-model hybrid Korean air quality forecasting system

ABSTRACTThe Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters’ capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters’ error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m3 and the R2 of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases.Implications: The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the national forecasting be improved. In this study, we investigated the problems in the current forecasting as well as various alternatives to solve the problems. Such efforts to improve the accuracy of the forecast are expected to contribute to the protection of public health by increasing the availability of the forecast system.

Reconsidering emissions of ammonia from chemical fertilizer usage in Midwest USA

AbstractWe present alternative methods for estimating spatial surrogates and temporal factors for ammonia (NH3) emissions from chemical fertilizer usage (CFU), in the USA, at spatial and temporal scales used to simulate regional air quality and deposition of reactive nitrogen to ecosystems. The newly developed Improved Spatial Surrogate (ISS) method incorporates year‐specific fertilizer sales data, high resolution and year‐specific crop maps, and local crop nitrogen demands to allocate NH3 emissions at 4 km × 4 km grid cells. Results are compared with the commonly used gridded emission estimates by the Sparse Matrix Operator Kernel Emissions (SMOKE) preprocessor. NH3 emissions over Central Illinois in the USA, estimated at the 4 km × 4 km grid level in SMOKE and ISS methods, exhibit differences between −10% and 120%, with 58% of the grid cells exhibiting more than ±10% difference. Application of the ISS method for a larger domain over the Midwest USA, at 4 km × 4 km, reflected similar differences. We also employed the Denitrification Decomposition (DNDC) model to develop daily temporal factors of NH3 emissions from CFU using multi‐site and multi‐year analyses. Ratio of temporal factors estimated by SMOKE and DNDC methods is 0.54 ± 2.35, with DNDC identifying daily emission peaks 2.5–8 times greater than SMOKE. Identified emission peaks will be useful for future air quality modeling efforts to understand particulate matter episodes, as well as trends in regional particulate matter formation and nitrogen deposition for Midwest USA, using the proposed NH3 emissions inventory.

수도권 지역에서 기상-대기질 모델링을 위한 VOC와 PM2.5의 화학종 분류 및 시간분배계수 산정

The purpose of this study is to assign emission source profiles of volatile organic compounds (VOCs) and particulate matters (PMs) for chemical speciation, and to correct the temporal allocation factor and the chemical speciation of source profiles according to the source classification code within the sparse matrix operator kernel emission system (SMOKE) in the Seoul metropolitan area. The chemical speciation from the source profiles of VOCs such as gasoline, diesel vapor, coating, dry cleaning and LPG include 12 and 34 species for the carbon bond IV (CBIV) chemical mechanism and the statewide air pollution research center 99 (SAPRC99) chemical mechanism, respectively. Also, the chemical speciation of PM2.5 such as soil, road dust, gasoline and diesel vehicles, industrial source, municipal incinerator, coal fired, power plant, biomass burning and marine was allocated to 5 species of fine PM, organic carbon, elementary carbon, , and . In addition, temporal profiles for point and line sources were obtained by using the stack telemetry system (TMS) and hourly traffic flows in the Seoul metropolitan area for 2007. In particular, the temporal allocation factor for the ozone modeling at point sources was estimated based on emission inventories of the stack TMS data.

Numerical study of the air quality in the city of Sofia - some preliminary results

Some extensive numerical simulations of the atmospheric composition fields in the city of Sofia have been recently performed. The simulations were carried out using the following set of models: the model WRF used as meteorological pre-processor; CMAQ - the community multiscale air quality system - the chemical transport model; SMOKE - the sparse matrix operator kernel emissions modelling system - the emission model. As the NCEP global analysis data with one degree resolution was used as meteorological background, the WRF and CMAQ nesting capabilities were applied for downscaling the simulations to a 1 km resolution over Sofia. The national emission inventory was used as an emission input for Bulgaria, while outside the country the emissions were taken from the TNO inventory. The biogenic emissions of VOC are estimated by the model SMOKE. Different characteristics of the numerically obtained concentration fields, including air quality indices will be demonstrated in the present paper.

Air quality simulations of wildfires in the Pacific Northwest evaluated with surface and satellite observations during the summers of 2007 and 2008

Abstract. Evaluation of a regional air quality forecasting system for the Pacific Northwest was carried out using a suite of surface and satellite observations. Wildfire events for the 2007 and 2008 fire seasons were simulated using the Air Information Report for Public Access and Community Tracking v.3 (AIRPACT-3) framework utilizing the Community Multi-scale Air Quality (CMAQ) model. Fire emissions were simulated using the BlueSky framework with fire locations determined by the Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation (SMARTFIRE). Plume rise was simulated using two different methods: the Fire Emission Production Simulator (FEPS) and the Sparse Matrix Operator Kernel Emissions (SMOKE) model. Predicted plume top heights were compared to the Cloud-Aerosol LIDAR with Orthogonal Polarization (CALIOP) instrument aboard the Cloud Aerosol LIDAR and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. Carbon monoxide predictions were compared to the Atmospheric InfraRed Sounder (AIRS) instrument aboard the Aqua satellite. Horizontal distributions of column aerosol optical depth (AOD) were compared to retrievals by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Aqua satellite. Model tropospheric nitrogen dioxide distributions were compared to retrievals from the Ozone Monitoring Instrument (OMI) aboard the Aura satellite. Surface ozone and PM2.5 predictions were compared to surface observations. The AIRPACT-3 model captured the location and transport direction of fire events well, but sometimes missed the timing of fire events and overall underestimated the PM2.5 impact of wildfire events at surface monitor locations. During the 2007 (2008) fire period, the fractional biases (FBs) of AIRPACT-3 for various pollutant observations included: average 24 h PM2.5 FB = −33% (−27%); maximum daily average 8 h ozone FB = −8% (+1%); AOD FB = −61% (−53%); total column CO FB = −10% (−5%); and tropospheric column NO2 FB = −39% (−28%). The bias in total column CO is within the range of expected error. Fractional biases of AIRPACT-3 plume tops were found to be −46% when compared in terms of above mean sea level, but only −28% when compared in terms of above ground level, partly due to the under-estimation of AIRPACT-3 ground height in complex terrain that results from the 12 km grid-cell smoothing. We conclude that aerosol predictions were too low for locations greater than ~100–300 km downwind from wildfire sources and that model predictions are likely under-predicting secondary organic aerosol (SOA) production, due to a combination of very low volatile organic compound (VOC) emission factors used in the United States Forest Service Consume model, an incomplete speciation of VOC to SOA precursors in SMOKE, and under-prediction by the SOA parameterization within CMAQ.

Impacts of different plant functional types on ambient ozone predictions in the Seoul Metropolitan Areas (SMAs), Korea

Abstract. Plant functional type (PFT) distributions affect the results of biogenic emission modeling as well as O3 and particulate matter (PM) simulations using chemistry-transport models (CTMs). This paper analyzes the variations of both surface biogenic volatile organic compound (BVOC) emissions and O3 concentrations due to changes in the PFT distributions in the Seoul Metropolitan Areas, Korea. The Fifth-Generation NCAR/Pennsylvania State Meso-scale Model (MM5)/the Model of Emissions of Gases and Aerosols from Nature (MEGAN)/the Sparse Matrix Operator Kernel Emissions (SMOKE)/the Community Multiscale Air Quality (CMAQ) model simulations were implemented over the Seoul Metropolitan Areas in Korea to predict surface O3 concentrations for the period of 1 May to 31 June 2008. Starting from a performance check of CTM predictions, we consecutively assessed the effects of PFT area deviations on the MEGAN BVOC and CTM O3 predictions, and we further considered the basis of geospatial and statistical analyses. The three PFT data sets considered were (1) the Korean PFT, developed with Korea-specific vegetation database; (2) the CDP PFT, adopted from the community data portal (CDP) of US National Center for Atmospheric Research in the United States (NCAR); (3) MODIS PFT, reclassified from the NASA Terra and Aqua combined land cover products. Although the CMAQ performance check reveals that all of the three different PFT data sets are applicable choices for regulatory modeling practice, noticeable primary data (i.e., PFT and Leaf Area Index (LAI)) was observed to be missing in many geographic locations. Based on the assessed effect of such missing data on CMAQ O3 predictions, we found that this missing data can cause spatially increased bias in CMAQ O3. Thus, it must be resolved in the near future to obtain more accurate biogenic emission and chemistry transport modeling results. Comparisons of MEGAN biogenic emission results with the three different PFT data showed that broadleaf trees (BTs) are the most significant contributor, followed by needleleaf trees (NTs), shrub (SB), and herbaceous plants (HBs) to the total BVOCs. In addition, isoprene from BTs and terpene from NTs were recognized as significant primary and secondary BVOC species in terms of BVOC emissions distributions and O3-forming potentials in the study domain. A geographically weighted regression analysis with locally compensated ridge (LCR-GWR) with the different PFT data (δO3 vs. δPFTs) suggests that addition of BT, SB, and NT areas can contribute to O3 increase, whereas addition of an HB area contributes to O3 decrease in the domain. Assessment results of the simulated spatial and temporal changes of O3 distributions with the different PFT scenarios reveal that hourly and local impacts from the different PFT distributions on occasional inter-deviations of O3 are quite noticeable, reaching up to 13 ppb. The simulated maximum 1 h O3 inter-deviations between different PFT scenarios have an asymmetric diurnal distribution pattern (low in the early morning, rising during the day, peaking at 05:00 p.m., and decreasing during the night) in the study domain. Exponentially diverging hourly BVOC emissions and O3 concentrations with increasing ambient temperature suggest that the use of different PFT distribution data requires much caution when modeling (or forecasting) O3 air quality in complicated urban atmospheric conditions in terms of whether uncertainties in O3 prediction results are expected to be mild or severe.