Source-oriented Air Quality Model Research Articles

Source apportionment of black carbon aerosols from light absorption observation and source-oriented modeling: an implication in a coastal city in China

Abstract. Black carbon (BC) is the most important light-absorbing aerosol in the atmosphere. However, sources of atmospheric BC aerosols are largely uncertain, making it difficult to assess its influence on radiative forcing and climate change. In this study, year-round light-absorption observations were conducted during 2014 using an aethalometer in Xiamen, a coastal city in Southeast China. Source apportionment of BC was performed and temporal variations in BC sources were characterized based on both light absorption measurements and a source-oriented air quality model. The annual average concentrations of BC from fossil fuel (BCff) and biomass burning (BCbb) by the aethalometer method were 2932 ± 1444 ng m−3 and 1340 ± 542 ng m−3, contributing 66.7 % and 33.3 % to total BC, respectively. A sensitivity analysis was performed with different absorption Ångström exponent (AAE) values of fossil fuel combustion (αff) and biomass burning (αbb), suggesting that the aethalometer method was more sensitive to changes in αbb than αff. BCbb contribution exhibited a clear diurnal cycle, with the highest level (37.9 %) in the evening rush hour and a seasonal pattern with the maximum (39.9 %) in winter. Conditional probability function (CPF) analysis revealed the large biomass-burning contributions were accompanied by east-northeasterly and northerly winds. Backward trajectory indicated that air masses from North and East–Central China were associated with larger biomass-burning contributions. Potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) suggested that North and East–Central China and Southeast Asia were potential sources of both BCff and BCbb. The source-oriented modeling results showed that transportation, residential and open biomass burning accounting for 45.3 %, 30.1 % and 17.6 % were the major BC sources. Among the three fuel catalogs, liquid fossil fuel (46.5 %) was the largest source, followed by biomass burning (32.6 %) and coal combustion (20.9 %). Source contributions of fossil fuel combustion and biomass burning identified by the source-oriented model were 67.4 % and 32.6 %, respectively, close to those obtained by the aethalometer method. The findings provide solid support for controlling fossil fuel sources to limit the impacts of BC on climate change and environmental degradation in the relatively clean region in China.

Quantifying primary and secondary humic-like substances in urban aerosol based on emission source characterization and a source-oriented air quality model

Abstract. Humic-like substances (HULIS) are a mixture of high-molecular-weight, water-soluble organic compounds that are widely distributed in atmospheric aerosol. Their sources are rarely studied quantitatively. Biomass burning is generally accepted as a major primary source of ambient humic-like substances (HULIS) with additional secondary material formed in the atmosphere. However, the present study provides direct evidence that residential coal burning is also a significant source of ambient HULIS, especially in the heating season in northern China based on source measurements, ambient sampling and analysis, and apportionment with source-oriented CMAQ modeling. Emission tests show that residential coal combustion produces 5 % to 24 % of the emitted organic carbon (OC) as HULIS carbon (HULISc). Estimation of primary emissions of HULIS in Beijing indicated that residential biofuel and coal burning contribute about 70 % and 25 % of annual primary HULIS, respectively. Vehicle exhaust, industry, and power plant contributions are negligible. The average concentration of ambient HULIS in PM2.5 was 7.5 µg m−3 in urban Beijing and HULIS exhibited obvious seasonal variations with the highest concentrations in winter. HULISc accounts for 7.2 % of PM2.5 mass, 24.5 % of OC, and 59.5 % of water-soluble organic carbon. HULIS are found to correlate well with K+, Cl−, sulfate, and secondary organic aerosol, suggesting its sources include biomass burning, coal combustion, and secondary aerosol formation. Source apportionment based on CMAQ modeling shows residential biofuel and coal burning and secondary formation are important sources of ambient HULIS, contributing 47.1 %, 15.1 %, and 38.9 %, respectively.

Evaluation of on-road vehicle CO and NOx National Emission Inventories using an urban-scale source-oriented air quality model

Abstract The MOBILE6.2 model was replaced by the Motor Vehicle Emission Simulator (MOVES) in 2012 as an official tool recommended by the United States Environmental Protection Agency (US EPA) to predict vehicular pollutant emission factors. In this study, on-road vehicle emission inventories of CO and NOx for Southeast Texas generated by MOVES and MOBILE6.2 in two versions of the 2005 National Emission Inventory (NEI) were studied by comparing predicted CO and NOx using the EPA's Community Multiscale Air Quality (CMAQ) Model incorporated with a source-oriented gas phase chemical mechanism with measurements made at six urban and industrial sites in Southeast Texas. The source tracing technique allows direct determination of contributions of on-road vehicles to overall CO and NOx concentrations and identification of ambient concentration measurements which are mostly impacted by vehicle emissions. By grouping the fractional bias (FB) values of the hourly predictions based on vehicle contributions to total CO or NOx concentrations, clear trends in the FB were observed, indicating systematic biases in the emission inventory for these species. Data points dominated by vehicle emissions suggest that surface CO concentrations due to vehicle exhaust are significantly over-estimated by a factor of 2 using either MOVES or MOBILE6.2. NOx concentrations are overestimated by approximately 20–35% and 70% by using the MOBILE6.2 and MOVES emissions, respectively. Emission scaling runs show that a domain-wide reduction of MOBILE6.2 CO emissions by 60% and NOx emissions by 15–25% leads to better model performance of exhaust CO and NOx concentrations in the current study.

PM2.5 co-benefits of climate change legislation part 1: California’s AB 32

The Scoping Plan for compliance with California Assembly Bill 32 (Global Warming Solutions Act of 2006; AB 32) proposes a substantial reduction in 2020 greenhouse gas (GHG) emissions from all economic sectors through energy efficiency, renewable energy, and other technological measures. Most of the AB 32 Scoping Plan measures will simultaneously reduce emissions of traditional criteria pollutants along with GHGs leading to a co-benefit of improved air quality in California. The present study quantifies the airborne particulate matter (PM2.5) co-benefits of AB 32 by comparing future air quality under a Business as Usual (BAU) scenario (without AB 32) to AB 32 implementation by sector. AB 32 measures were divided into five levels defined by sector as follows: 1) industrial sources, 2) electric utility and natural gas sources, 3) agricultural sources, 4) on-road mobile sources and 5) other mobile sources. Air quality throughout California was simulated using the UCD source-oriented air quality model during 12 days of severe air pollution and over 108 days of typical meteorology representing an annual average period in the year 2030 (10 years after the AB 32 adoption deadline). The net effect of all AB 32 measures reduced statewide primary PM and NOx emissions by ~1 % and ~15 %, respectively. Air quality simulations predict that these emissions reductions lower population-weighted PM2.5 concentrations by ~6 % for California. The South Coast Air Basin (SoCAB) experienced the greatest reductions in PM2.5 concentrations due to the AB 32 transportation measures while the San Joaquin Valley (SJV) experiences the smallest reductions or even slight increases in PM2.5 concentrations due to the AB 32 measures that called for increased use of dairy biogas for electricity generation. The ~6 % reduction in PM2.5 exposure associated with AB 32 predicted in the current study reduced air pollution mortality in California by 6.2 %, avoiding 880 (560–1100) premature deaths per year for the conditions in 2030. The monetary benefit from this avoided mortality was estimated at $5.4B/yr with a weighted average benefit per tonne of $35 k/tonne ($23 k/tonne–$45 k/tonne) of PM, NOx, SOx, and NH3 emissions reduction.

Modeling air quality during the California Regional PM 10/PM 2.5 Air Quality Study (CPRAQS) using the UCD/CIT source-oriented air quality model – Part III. Regional source apportionment of secondary and total airborne particulate matter

A comprehensive air quality modeling project was carried out to simulate regional source contributions to secondary and total (=primary + secondary) airborne particle concentrations in California's Central Valley. A three-week stagnation episode lasting from December 15, 2000 to January 7, 2001, was chosen for study using the air quality and meteorological data collected during the California Regional PM10/PM2.5 Air Quality Study (CRPAQS). The UCD/CIT mechanistic air quality model was used with explicit decomposition of the gas phase reaction chemistry to track source contributions to secondary PM. Inert artificial tracers were used with an internal mixture representation to track source contributions to primary PM. Both primary and secondary source apportionment calculations were performed for 15 size fractions ranging from 0.01 to 10 μm particle diameters. Primary and secondary source contributions were resolved for fugitive dust, road dust, diesel engines, catalyst equipped gasoline engines, non-catalyst equipped gasoline engines, wood burning, food cooking, high sulfur fuel combustion, and other anthropogenic sources. Diesel engines were identified as the largest source of secondary nitrate in central California during the study episode, accounting for approximately 40% of the total PM2.5 nitrate. Catalyst equipped gasoline engines were also significant, contributing approximately 20% of the total secondary PM2.5 nitrate. Agricultural sources were the dominant source of secondary ammonium ion. Sharp gradients of PM concentrations were predicted around major urban areas. The relative source contributions to PM2.5 from each source category in urban areas differ from those in rural areas, due to the dominance of primary OC in urban locations and secondary nitrate in the rural areas. The source contributions to ultra-fine particle mass PM0.1 also show clear urban/rural differences. Wood smoke was found to be the major source of PM0.1 in urban areas while motor vehicle sources were the major contributor of PM0.1 in rural areas, reflecting the influence from two major highways that transect the Valley.

Modeling air quality during the California Regional PM 10/PM 2.5 Air Quality Study (CRPAQS) using the UCD/CIT source-oriented air quality model – Part I. Base case model results

Abstract A comprehensive air quality modeling project was carried out to simulate size and composition resolved airborne particulate matter concentrations in northern and central California using the pollutant concentration and meteorological data collected during the California Regional PM10/PM2.5 Air Quality Study (CRPAQS) from December 15, 2000 to January 7, 2001. Measured 24-h average PM2.5 concentrations during this time period exceeded 180 μg m−3 at Bakersfield, making it the most severe PM2.5 air quality episode ever recorded in the United States with a rigorous measurement database to support modeling. In this paper, the UCD/CIT source-oriented air quality model is used to predict the concentrations of O3, NO, NO2, CO, elemental carbon (EC), organic compounds (OC), nitrate and PM2.5 mass concentration over a 24-day period using a horizontal resolution of 4 km × 4 km to cover all of central California. This is the first extensive evaluation of an air quality model in central California using the fine spatial resolution appropriate for the mountain–valley topography of the region combined with the relatively long multi-week time scales associated with winter stagnation events. Fractional bias (FB) values were calculated at all sites on each day of the study to quantify model performance. The CO (FB = −0.5 to +0.3), O3 (FB = −0.5 to +0.25), NO (FB = −0.9 to −0.1) and NO2 (FB = 0 to +0.4) concentrations predicted by the UCD/CIT model are in general agreement with observations at most monitoring stations throughout the Valley. The predicted PM2.5 concentrations (FB = −0.5 to +0.75) generally agree with observations at Bethel Island, Sacramento, Fresno and Bakersfield spanning the entire length of the model domain. PM2.5 concentrations are over-predicted at the remote monitoring site Angiola in the central portion of the domain. Part of the over-prediction is due to excess fugitive dust emissions. CO, NO, EC and OC were all under-predicted at Angiola, indicating possible missing combustion sources in the emission inventory. The regional nitrate (FB = −1.5 to +1.25) formation dynamics were correctly reproduced by the model simulation but imperfect wind fields cause differences between the predicted vs. measured spatial distribution of nitrate during the last several days of simulation leading to the broader range of fraction bias. Overall, the results of the current study confirm the ability of the air quality model to capture the major features of a severe particulate air pollution event in northern and central California providing a foundation for future studies on source apportionment and emissions control.

Modeling air quality during the California Regional PM 10/PM 2.5 Air Quality Study (CPRAQS) using the UCD/CIT Source Oriented Air Quality Model – Part II. Regional source apportionment of primary airborne particulate matter

A comprehensive air quality modeling project was carried out to simulate regional source contributions to primary airborne particle concentrations in California's central Valley. A 3-week stagnation episode lasting from December 15, 2000 to January 7, 2001, was chosen for study using the air quality and meteorological data collected during the California Regional PM10/PM2.5 Air Quality Study (CRPAQS). The UCD/CIT source oriented air quality model was applied to this episode using both the source-oriented external mixture configuration and an internal mixture with artificial tracers so that source contribution information could be retrieved in less time. The majority of the predicted and measured primary airborne particulate matter mass was composed of elemental carbon (EC) and organic carbon (OC). Previous work has shown that base case EC and OC predictions made by the UCD/CIT model are in good agreement with observations. Model results from the current study show that the highest EC and OC concentrations occur in urban areas and along transportation corridors where primary emissions are largest. Lower concentrations of primary EC and OC are predicted at rural locations in the San Joaquin Valley (SJV). Source contributions predicted by the UCD/CIT air quality model were compared to receptor-oriented source apportionment results produced by the Chemical Mass Balance (CMB) model at Fresno and Angiola. The relative contributions from major sources predicted by the UCD/CIT model agree with the CMB model results, building confidence in the accuracy of the UCD/CIT model predictions at locations where the CMB results are not available. Wood smoke was identified as the major regional source of primary OC in airborne particles in the winter SJV episode, accounting for approximately 50% of the total PM2.5. Diesel engines were also found to be a significant contributor to primary PM2.5 OC and the largest contributor to the predicted PM2.5 EC averaged over a typical day. EC contributions from wood smoke increased at night and sometimes reached as high as 40% of the total PM2.5 EC. The contribution of fugitive dust to primary PM2.5 mass was also predicted to be significant, especially in rural areas, but this result is likely biased high by the lack of an appropriate diurnal profile for dust emissions. The results of the current study suggest that reductions in wood burning and diesel engine activity would reduce the regional concentration of primary PM2.5 during severe winter stagnation events in the SJV.

Modeling particulate matter in the San Joaquin Valley with a source-oriented externally mixed three-dimensional photochemical grid model

Air pollution in California's San Joaquin Valley (SJV) rivals that of the Los Angeles area and ranks among the worst in the United States for particulate matter (PM) and ozone. The application and validation of an atmospheric chemical transport model to the SJV will aid in the design of emissions control programs to improve air quality. The extensive data required for meaningful Eulerian modeling of airborne PM in the SJV region were collected as a part of the 1995 Integrated Monitoring Study (IMS95). In the current study, the CIT-UCD source-oriented air quality model is applied to the IMS95 data set to verify model performance in the SJV. This article represents the first published application of a full-scale photochemical grid model with diagnostic meteorological data to simulate PM concentrations in the SJV and is the first study outside of the Los Angeles area to include complete PM model performance statistics. The CIT-UCD model results show excellent agreement with most measurements collected during the IMS95 4–6 January 1996 modeling episode. The fractional bias (FBIAS) for SJV ozone and PM 10 are approximately 0.16 and −0.19, respectively, for all SJV stations excluding Bakersfield. Most modeled criteria gases, PM precursor gases, and chemically speciated PM concentrations show strong agreement with their corresponding measurements at the Chowchilla, Fresno, Kern Wildlife Refuge, and Bakersfield sampling stations. Furthermore, the modeled PM size distribution of nitrate, ammonium ion, and sulfate agree well with cascade impactor measurements made at Bakersfield on 5 January 1996. Given the robust model agreement with both gas and condensed phase measurements, it appears that the CIT-UCD model adequately captures the fundamental transport and chemical reactivity of air pollutants in the IMS95 domain during a typical severe pollution episode. These results suggest that the CIT-UCD model can be used to explore control scenarios designed to improve air quality in the SJV.

Source apportionment of visibility impairment using a three-dimensional source-oriented air quality model.

A three-dimensional source-oriented Eulerian air quality model is developed that can predict source contributions to the visibility reduction. Particulate matter and precursor gases from 14 different sources (crustal material, paved road dust, diesel engines, meat cooking, noncatalyst-equipped gasoline engines, catalyst-equipped gasoline engines, high-sulfur fuel, sea salt, refrigerant losses, residential production, animals, soil and fertilizer application, other anthropogenic sources, and background sources) are tracked though a mathematical simulation of emission, chemical reaction, gas-to-particle conversion, transport, and deposition. A visibility model based on Mie theory is modified to use the calculated source contributions to airborne particulate matter size and composition as well as gas-phase pollutant concentrations to quantify total source contributions to visibility impairment. The combined air quality-visibility model is applied to predict source contributions to visibility reduction in southern California for a typical air pollution episode (September 23-25, 1996). The model successfully predicts a severe visibility reduction in the eastern portion of the South Coast Air Basin where the average daytime visibility is measured to be less than 10 km. In the relatively clean coastal portion of the domain, the model successfully predicts that the average daytime visibility is greater than 65 km. Transportation-related sources directly account for approximately 50% of the visibility reduction (diesel engines approximately 15-20%, catalyst-equipped gasoline engines approximately 10-20%, noncatalyst-equipped gasoline engines approximately 3-5%, crustal and paved road dust approximately 5%) in the region with the most severe visibility impairment. Ammonia emissions from animal sources account for approximately 10-15% of the visibility reduction.