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ItemModeling of factors impacting atmospheric formic acid, ozone and PM2.5 dynamics(Georgia Institute of Technology, 2023-04-27) Gao, ZiqiElevated concentrations of air pollutants have been linked to an increased risk of respiratory diseases, cardiovascular diseases, lung cancer, and mortality rate. Multiple atmospheric processes, such as emissions, chemical reactions, transport, and deposition, can affect ozone formation, particulate matter (PM2.5), and organic acids. Although the government has set many regulations to reduce the emissions of pollutant precursors, the actual impacts of these regulations are highly sensitive to external factors such as local meteorology, source distributions, and large-scale climatic patterns, such that emissions reductions could be ineffective or could even result in worse air pollution. The effectiveness of the regulations can be evaluated after isolating the impacts of external factors. This can help policymakers make future regulations to mitigate air pollutant concentrations more effective and efficient. This work applied several models, ranging from chemical transport, box, and empirical models, to assess the impact of existing national and state emission reduction regulations on emissions and air pollutants concentrations. These models were also used to evaluate and quantify the impact of the main drivers and processes (e.g., emissions, meteorological impacts, chemical reactions, etc.) that impact air quality and predict potential air pollution concentrations in future years. In looking at the impact on both anthropogenic and biogenic emissions, we found that 1) A bi-directional emission-deposition process has more impact on formic acid formation than photooxidation reactions, 2) Emissions largely control peak ozone and PM2.5 concentrations as well as PM2.5 chemical composition trends, though meteorology impacts daily variability and can lead to increases in annual peak ozone concentrations despite emissions reductions. 3) In the future, meteorological impacts will significantly impact all air pollutant concentrations with the emissions reduction and affect the attainment of pollutant standards. Thus, while historical and future air pollution regulations can (or, in some cases, have) attain pollutant standards, meteorology, and climatic effects may endanger consistent attainment.
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ItemCombining Air Quality Modeling and Observations for Policy-Relevant Applications(Georgia Institute of Technology, 2023-04-25) Skipper, Tommy NashChemical transport models (CTMs) are numerical models which simulate atmospheric chemistry and physics based on first principles. CTMs are used to estimate the impacts of air quality policies and regulations as well as for more fundamental research. CTMs can be biased compared to observed air pollutant concentrations; however, observational data can be combined with CTMs to improve model performance. Here, several applications of CTMs for policy-relevant analyses are studied and opportunities for incorporating observations to complement CTM results are explored. Specific applications are the air quality impacts of electric vehicle (EV) adoption in California, air quality impacts near ports in the Los Angeles area resulting from supply chain disruption caused by the Covid-19 pandemic, and US background ozone. A CTM was applied to quantify the air quality co-benefits of EV adoption in California. Air quality benefits scaled approximately linearly with increasing EV adoption. The simulated population-weighted statewide average PM2.5 concentration was reduced by about 0.5 µg/m3 in a scenario with 100% adoption of EVs. Impacts of container ship backlogs at the Ports of Los Angeles and Long Beach were analyzed using a combination of satellite-based observations and CTM simulations to separate meteorological and emission impacts. After adjusting for year-to-year fluctuations in meteorology, a 28% emission-related increase in tropospheric NO2 column totals was found in areas immediately downwind of the ports. An observation-model data fusion approach was developed to apportion CTM biases to US background and US anthropogenic sources and to reduce ensemble variation of US background ozone estimates. This approach was extended to estimate CTM biases in specific sources of US background ozone including naturally occurring sources, anthropogenic pollution from outside the US, and stratospheric ozone. Overall, results show that CTMs can provide useful policy-relevant information as can observations but that sometimes more information can be gained by combining observations and CTM results than either can provide in isolation.
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ItemDevelopment and Application of Data Fusion and Source Apportionment Methods over the Contiguous United States(Georgia Institute of Technology, 2022-07-20) Senthilkumar, NirupamaExposure to air pollution has been linked to numerous adverse health effects such as cardiovascular diseases, pulmonary diseases, cancer, and increased morbidity. Having accurate air quality exposure estimates are important to understanding the drivers of negative health outcomes. Air quality simulations and observational data are used as inputs in health analysis to estimate exposure to air pollution. However, observational data are limited spatially and temporally while air quality simulated data have biases associated. This dissertation presents multiple computational techniques to provide spatiotemporally accurate and complete air quality and source impacts fields for health analysis. A data fusion method along with a random forest technique is used to generate fused fields for particulate, gas, and trace metal species at a 12km resolution for the years 2005-2014. The data fusion method combines gridded simulations from the community multiscale air quality (CMAQ) model and point source observational data to create more accurate spatiotemporally complete air quality fields. The data fusion method creates high temporal correlations at observational locations for all species studied. The random forest approach uses land use variable information to correct spatial bias in annual average for fused field products. The data fusion and random forest method showed large improvements in spatial and temporal correlation for major particulate and gas species, and moderate improvements for trace metal pollutants. The fused field products were then used in a receptor model source apportionment analysis for particulate matter. A receptor model, chemical mass balance with gas constraints (CMBGC), was applied in each 12km fused field grid cell to generate spatiotemporally complete source impact fields for 10 particulate matter sources: gasoline vehicles, diesel vehicles, dust, biomass burning, coal combustion, ammonium sulfate, ammonium bisulfate, ammonium nitrate, secondary organic carbon, and sea salt. A CMBGC model was also applied to each 12km CMAQ grid cell to compare the improvements made in source impact fields from applying the data fusion and random forest correction. The comparison showed that data fusion was necessary to produce accurate source impact fields. The implications from this research show that data fusion can provide large improvements in air quality fields for health analysis. Fused fields are also able to provide spatiotemporally complete particulate matter source impact fields that match source impacts generated from observations. The daily data fused fields for 22 species and daily source impact fields are made available for future health and air quality analysis.
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ItemQuantifying the Impacts of Anthropogenic Emissions and Specific Infrastructures on Urban Air Quality(Georgia Institute of Technology, 2021-08-30) Lawal, Abiola S.The interconnectivity between city infrastructure, energy and air quality is explored by evaluating the impact of environmental regulations, urban layout, and the transportation sector on air quality and energy use. Particular aspects of the research include assessing how controls have impacted aerosol acidity (which impacts health), linkages between energy, demographics, and how both airports and the use of autonomous and electric vehicles may impact on air quality. This research finds that while environmental regulations are effective in curbing pollution, as measured through decreases in fine particulate matter (PM2.5) emissions in the U.S., PM2.5 particles (aerosol) remain acidic. An implication of this is that it could be decades before changes in aerosol acidity, which is related to the toxicity and adverse health impacts of PM2.5, are seen. The research also found a strong statistical relationship between residential energy (electric and natural gas) consumption and socio-economic demographic (SED) factors for Zip Code Tabulated Areas (ZCTAs) in metropolitan Atlanta. However the electricity model exhibited high bias. Additional analyses found that electricity use is affected by the urban morphology of the roadways, with ZCTAs in high road density areas using more electricity The impacts of airports, mainly the Atlanta Hartsfield Jackson (ATL) on air quality, was examined using fine scale chemical transport modeling (CMAQ).CMAQ results are evaluated using ground-based and high resolution satellite-based observations from the TROPOspheric Monitoring Instrument (TROPOMI). TROPOMI's ability to provide consistent NO2 vertical column densities (VCDs) is assessed using the CMAQ results around two power plants. A 3D airport emission inventory from full flight operations is developed and compared against a base inventory with only surface airport operation emissions allocated to ATL. Results show that the magnitude and spatial extent of airport effects on air quality would be understated if only the base inventory is used for regulatory purposes. Lastly, we assess the efficacy of an electrified automated fleet of passenger cars on 2050 air quality in the US with a 2050 scenario where gasoline powered passenger cars emit lower levels of pollution than present day automobiles with CMAQ. We find that electric cars have advantages over future gasoline vehicles in terms of improving air quality, though the magnitude varies by species (O3, PM2.5). The overall implications of our findings is that policy, technology and urban infrastructure have a compounded effect on the efficacy of environmental regulations, air quality and energy use. Multiple factors should be considered when designing policies promoting equitable, sustainable cities.
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ItemSignificant contrasts of aerosol acidity between China and the United States(Georgia Institute of Technology, 2021-04-22) Zhang, BingqingAerosol acidity governs several key processes in aerosol physics and chemistry, thus affecting aerosol mass and composition, and ultimately the climate and human health. Previous studies have reported aerosol pH values separately in China and the United States (US), implying different aerosol acidity between these two countries. However, there is debate about whether mass concentration or chemical composition is the more important driver of differences in aerosol acidity. A full picture of the pH difference and the underlying mechanisms responsible for it is hindered by the scarcity of simultaneous measurements of particle composition and gaseous species, especially in China. Here we conduct a comprehensive assessment of aerosol acidity based on annual mean data in China and the US using extended ground-level measurements and regional chemical transport model simulations. We show that annually aerosol in China is significantly less acidic than in the US, with pH values 1–2 units higher. Based on a proposed multivariable Taylor Series method and a series of sensitivity tests, we identify major factors that potentially leading to the pH difference. Compared to the US, China has much higher aerosol mass concentrations (gas + particle, by a factor of 8.4 on average) and a higher fraction of total ammonia (gas + particle) in the aerosol composition. Our assessment shows that such differences in mass concentrations and chemical composition play equally important roles in driving the aerosol pH difference between China and the US. Therefore, both the facts that China is more polluted than the US and is rich in ammonia together explain the aerosol pH difference. The difference in aerosol acidity highlighted in the present study implies potential differences in formation mechanisms, physicochemical properties, and toxicity of aerosol particles in these two countries.
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ItemEffects of ammonia emissions from agricultural sources on air quality and sensitive ecosystems in the United States(Georgia Institute of Technology, 2020-07-09) Chen, YilinAmmonia (NH3) emissions from agricultural practices, including fertilizer application and livestock waste management, significantly disturbs the global nitrogen cycling. The excessive emission of NH3 causes a series of adverse impacts on air quality, human health, and ecosystem well-being. Future response of air quality and nitrogen loading in sensitive ecosystems to agricultural NH3 emission increase in not well understood, especially at the national or regional scale. This thesis presents the development, evaluation, and application of an integrated modeling framework to estimate the future response of PM2.5 (particulate matter with an aerodynamic diameter of 2.5 micrometers or less) formation and reactive nitrogen (Nr) deposition to the rising agricultural NH3 emission in the United States when other conventional pollutants are being further regulated. Present-day and future scenarios are designed to reflect emission and meteorological condition changes between 2011 and 2050. First, ambient PM2.5 acidity, mass concentration, and chemical speciation are simulated for present-day and future scenarios using the Community Multiscale Air Quality Model (CMAQ). Additional sensitivity scenarios are tested to explore the response of aerosol formation to different levels of anthropogenic SO2 emission control and boundary conditions. The results show that the aerosol will remain acidic in the future even with an aggressive reduction of anthropogenic SO2 emissions and an increase in NH3 emissions in the most of the U.S., because of the buffering effect of NH3-NH4+, as well as the increased relative contribution from background SO42-. Second, by comparing the simulated Nr deposition with critical loads, the impact of Nr deposition on sensitive ecosystems are evaluated. The evaluation identifies that areas with intensive NH3 emission from agricultural practices will remain in exceedance despite effective NOx emission control. Reducing the nitrogen deposition level below the critical loads requires simultaneous control of NH3 emissions from agriculture activities and NOx emissions from fossil fuel combustion. The Nr deposition is further linked with a water quality model to locate watersheds vulnerable to agricultural NH3 emission increase. Recognizing the model-based assessements can be biased by the high uncertainty in inventoried NH3 emissions, the thesis constrains the NH3 emission estimates from the National Emission Inventory (NEI) using satellite observations and hybrid inverse modeling with adjoint model of CMAQ. The optimized NH3 emission in April is 46% higher than the NEI estimates with large spatial differences. Model evaluation against independent ground observations suggests the optimized NH3 emissions reduce model bias. Re-evaluation of PM2.5 concentration and Nr deposition with the optimized NH3 emissions shows the optimization can partly close the gap between simulated and observed PM2.5 concentration by reducing the bias in simulated NH4NO3 in April. The Nr deposition in sensitive ecosystems might be underestimated by 40% due to the low bias in NH3 emission. The optimized NH3 emission and NEI estimates are in good agreement in July and October. The integrated modeling framework and simulations presented in this thesis provide valuable information for setting priority regions for agricultural NH3 emissions regulation.
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ItemImproved air quality from sustainable city development in the United States, India, and China(Georgia Institute of Technology, 2020-03-05) Lal, Raj M.It is estimated that ~70% of people will live in cities by 2050, an increase of 2.5 billion globally. Because of such growth, there are pressing needs to study sustainable city management, develop and utilize new methods to obtain fine-scale data, and identify infrastructure to support future development to improve public health. Exposure to ambient air pollution is associated with adverse health outcomes and is one of the leading causes of premature mortality globally, estimated to contribute to 6.5 million deaths each year, many of which occur in the United States (~70,000), India (1.4 million), and China (~650,000). In addition, these three countries are the top-three CO2-emitting countries globally, accounting for ~50% of global emissions. The work presented in this thesis explores strategies to improve ambient air quality, reduce carbon emissions, assess PM2.5 spatial patterns in US cities, and study fine-scale linkages between various environmental indicators. The Taj Mahal is an iconic Indian monument and one of the Seven Wonders of the World but its marble surface has been discolored with time. We used spatially detailed emission estimates and air quality modeling to estimate biomass (e.g., municipal solid waste (MSW), dung cake, wood, and crop) burning contributions to the discoloration. National Chinese PM2.5 and CO2 emission reductions from novel, urban-industrial symbiosis strategies were assessed, including waste heat re-use from electric generating and industrial sources. The relationship between air quality, neighborhood infrastructure, and subjective well-being was characterized to provide supportive data for more equitable outcomes for future city development. PM2.5, NO2, and CO concentrations in the near-road environment were compared to concentrations at nearby urban monitors in US cities and we found no statistically significant (α=0.05) difference of PM2.5 between the two environments. Finally, power plant and industrial waste heat to electricity and coal fly-ash material exchanges were assessed in India to estimate air pollution and carbon mitigation of such strategies there.
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ItemEffects of prescribed burning on air quality in the southeastern U.S. and implications for public health studies(Georgia Institute of Technology, 2019-04-02) Huang, RanBiomass burning is an important global source of gases and aerosols, e.g., carbon monoxide, carbon dioxide, PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) and black carbon. These products, generally referred to as "smoke" can reduce visibility and have adverse health effect. Prescribed burning, a type of biomass burning, is a land management practice used in the U.S. to reduce wildfire risk and maintain healthy ecosystems. This dissertation is a presentation of research quantifying the impact of prescribed burning on air quality and human health in the southeastern U.S., the most active prescribed burning area in the U.S. Considering the potential impacts, the estimation of prescribed burning emissions is crucial. However, current satellite-derived products have limitations in estimating the burned areas of small fires and still need improvements. Another need is to split the combined prescribed fire impact derived from chemical transport models (CTMs) into individual fire impacts. A novel source apportionment method (Dispersive Apportionment of Source Impacts) has been developed for this by using concentration fields derived from dispersion modeling. Individual burn impacts obtained in this manner could help local land and air quality managers decide which burns should be allowed or restricted based on their impacts on air quality and public health in areas of concern. The feasibility of applying low-cost PM sensors for the detection of fire impacts has been evaluated. It was found that low-cost PM sensors can provide spatial information that is missed by a sparse regulatory monitoring network and, in combination with CTM simulations, they can be used in preparing high accuracy exposure fields needed for health assessments. Data fusion is a method that integrates observations from sensors/monitors with simulations from CTM to better estimate ground-level air pollutant concentrations. The method has been applied in North Carolina from 2006 to 2008 to support the health analysis of coronary heart disease patients by developing spatiotemporal exposure fields. It has also been utilized to generate exposure fields to smoke from prescribed fire. These fields have been input to a health impact function for asthma-related Emergency Room Visits to find the health impact due to the prescribed fire in Georgia during burning season from 2015 to 2018. The spatial and temporal variations of health impacts from prescribed burning illustrate the importance of distinguishing seasons and areas when studying the relationship between exposure to pollutants from prescribed fire and its health effects. Overall, the methods and results presented in this dissertation improve the understanding of the impact of prescribed burning on air quality and human health. The data generated would also benefit future health epidemiological studies. The work presented could be useful to scientists and policy makers interested in prescribed fire and air quality, and inspire further research.
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ItemCharacterizing traffic-related air pollutant dynamics in a near-road environment(Georgia Institute of Technology, 2018-11-14) Moutinho, Jennifer LynnDetailed measurements and dispersion modeling were conducted to develop more accurate integrated metrics to assess exposure to potentially high pollutant levels of primary traffic emissions. A 13-week intensive sampling campaign was conducted at six monitoring sites surrounding one of the busiest highway segment in the US with the study area focusing on the Georgia Institute of Technology campus to capture the heterogeneity in pollutant concentrations related to primary traffic emissions. Differences in temporal pollutant concentrations at two near-road monitoring sites along the same road segment showed microenvironment characteristics are a driving factor in observations. Multipollutant metrics and dispersion modeling are two ways to quantify exposure to mobile source emissions. A statistical and biological metric provided insight on how they could be applied in future near-road studies. A dispersion model (R-LINE) was used to develop spatial concentration fields at a fine-spatial resolution over the area of primary exposures. To correct for high near-road bias, the R-LINE results were calibrated using measurement observations after the urban background was removed. Performing the calibration hourly also reduced the bias observed in the diurnal profile. Both the measurement observations and dispersion modeling results show that the highway has a substantial impact on primary traffic pollutant concentrations and captures the prominent spatial gradients across the campus domain, though the gradients were highly species dependent. These improved concentration fields were used to enhance the characterization of pollutant spatial distribution around a traffic hotspot and to quantify personal exposure to primary traffic emissions.
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ItemParticle emissions from consumer level 3D printers(Georgia Institute of Technology, 2018-11-13) Zhang, QianAs 3D printing technologies becoming popular and available to the general public, concerns have been raised on the emissions and potential health impacts of operating 3D printers in indoor environments. This research involved a comprehensive study of the particle emissions from consumer level 3D printers. Particle emissions were characterized using a standard test method developed for laser printers in an environmental chamber. The factors affecting particle emissions, such as printer brand, print filament material, brand and color, extrusion and build plate temperature, were systematically investigated. Acrylonitrile butadiene styrene (ABS) material, emitted orders of magnitude more particles than polylactic acid (PLA) in general. To understand the particle formation mechanism, an aerosol dynamic model was used to simulate the steady state particle characteristics during printing. This model linked the observed particle concentration distributions to the model parameters of precursor gas properties, and explained the contrasts among the most important controlling factors. Finally, multiple approaches were applied to assess particle toxicity. A consistency among various methods showed that PLA emitted particles induced similar levels of responses at much lower doses than particles generated from ABS filaments. However, calculations for the overall exposure showed ABS filaments may be more harmful due to their much higher emissions. Overall, 3D printers are sources of high levels of ultrafine particles, which are potentially harmful for their users, suggesting that methods to mitigate emissions should be considered and exposures minimized.