No description available.

No description available.

No description available.

Ever since air pollution became a national concern in the 1950's, more and more emphasis has been placed on collection of representative air samples for many purposed, to include (1) evaluation of the degree to which national ambient air quality standards are being met and (2) to monitor maximum emission levels from point sources. Until recently efforts were directed toward qualitative methods of siting monitors for representative sampling. Since the dispersion of effluents is most complex, the quality of the data collected on the basis of judgment and, more or less, incremental siting about the source, has become suspect. Furthermore, with the increasing demands for monitoring due to international growth in network monitoring systems, amendments to the Clean Air Act and the legislation on the Prevention of Significant Deteoriation of Air Quality, it is not cost-effective to encircle point sources with large numbers of equally spaced monitors. This paper discussed the history of air pollution concerns that have resulted in the need for monitoring; the development of siting techniques through largely qualitative measures; and finally, summarizes three quantitative methodologies for monitoring point sources. Emphasis is placed on the methodology developed by Noll, et al., (1977), based on the author's belief that this methodology represents the state of the art.

Accurate simulations of the global radiative impact of anthropogenic emissions must employ a tropospheric chemistry model that predicts realistic distributions of aerosols of all types. The need for a such a comprehensive yet computationally efficient tropospheric chemistry model is addressed in this research via systematic development of the various sub-models/mechanisms representing the gas-, aerosol-, and cloud-phase chemistries. The gas-phase model encompasses three tropospheric chemical regimes - background and urban, continental rural, and remote marine. The background and urban gas-phase mechanism is based on the paradigm of the Carbon Bond approach, modified for global-scale applications. The rural gas-phase chemistry includes highly condensed isoprene and a-pinene reactions. The isoprene photooxidation scheme is adapted for the present model from an available mechanism in the literature, while an a-pinene photooxidation mechanism, capable of predicting secondary organic aerosol formation, is developed for the first time from the available kinetic and product formation data. The remote marine gas- phase chemistry includes a highly condensed dimethylsulfide (DMS) photooxidation mechanism, based on a comprehensive scheme available in the literature. The proposed DMS mechanism can successfully explain the observed latitudinal variation in the ratios of methanesulfonic acid to non-sea-salt sulfate concentrations. A highly efficient dynamic aerosol growth model is developed for condensing inorganic gases. Algorithms are presented for calculating equilibrium surface concentrations over dry and wet multicomponent aerosols containing sulfate, nitrate, chloride, ammonium, and sodium. This alternative model is capable of predictions as accurate for completely dissolved aerosols, and more accurate for completely dry aerosols than some of the similar models available in the literature. For cloud processes, gas to liquid mass-transfer limitations to aqueous-phase reactions within cloud droplets are examined for all absorbing species by using the two-film model coupled with a comprehensive gas and aqueous-phase reaction mechanisms. Results indicate appreciable limitations only for the OH, HO₂, and NO₃ radicals. Subsequently, an accurate highly condensed aqueous-phase mechanism is derived for global-scale applications. / Ph. D.

Air quality in the Shenandoah Valley has deteriorated in recent years. The valley exceeds the National Ambient Air Quality Standards for ozone (O3) a few days each year, and with stricter fine particulate matter (PM2.5) standards coming into effect, the valley risks exceeding those as well. Visibility is poor in the valley region, and the haze obscures the spectacular vistas from the Shenandoah National Park. To solve the growing problem local governments in the valley joined forces to find economically and politically feasible ways to reduce air pollution. In this study we aim to provide the scientific basis for air quality management strategies through modeling the sensitivity of various pollutants to changes in emissions. We distinguish between locally generated versus regionally transported air pollution as well as assess the impacts of proposed local air pollution control measures on ambient air quality in the valley. The first part of this thesis assesses air pollutant emissions in the Shenandoah Valley. Emissions were assigned to one of 14 source categories and allocated by county or city. Biogenic sources were responsible for 56% of the volatile organic compounds (VOCs) emitted in the valley. VOCs are important because they, together with nitrogen oxides (NOx) react to form O3 in the presence of sunlight. On-road and off-road mobile sources were the largest anthropogenic sources of VOCs as well as 63% of the NOx. PM2.5 emissions were not dominated by any single source, but fuel combustion, dust, and agriculture were important contributors. The second part of this thesis focuses on modeling ambient air pollution concentrations in the Shenandoah Valley based on the emissions generated in the first portion. We developed a set of three alternative emissions scenarios for comparison to the base case. We first zeroed anthropogenic emissions in the valley, allowing us to determine how much pollution was produced by local sources versus transported into the valley from upwind areas. We then developed a scenario that contained nine different pollution reduction strategies being considered by local governments. Finally we modeled a similar scenario in which we predicted the impact of ten proposed greenhouse gas reduction strategies on concentrations of O3 and PM2.5. We found that PM2.5 concentrations fell when emissions in the valley were reduced, but O3 did not. PM2.5 concentrations fell by 26-57% for the Zero Case and by 10-27% for the other two cases, depending on the time of year and location. Conversely for O3 there was either no change in most seasons or a small increase in concentrations in the fall. These results suggest that PM2.5 in the valley can be controlled with local measures but O3 is a more geographically wide problem. / Master of Science

With the ongoing COVID-19 pandemic being spread all over the world, lockdown measures are being implemented making air pollution levels go down in several countries. In this context, the air quality changes in the highly populated and trafficked Brazilian states of Sao Paulo (SP) and Rio de Janeiro (RJ) are hereby going to be addressed using a combination of satellite and ground-based data analysis. We explored nitrogen dioxide (NO2) and particulate matter (PM2.5) daily levels for the month of May during different years within 2015-2020. Daily measurements of NO2 column concentrations from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura satellite were also gathered and averaged decreases of 42% and 49.6% were found for the year of 2020 compared to previous averaged 2015-2019 years. In parallel to the NO2 column retrieval, the ground-based data, measured by the Brazilian States Environmental Institutions, is analyzed, and correlated with satellite retrievals. Correlation coefficients between column and ground-based concentrations were 77% and 53% in SP and RJ, respectively. It was found a 13.3% (p-value = 0.099) and 18.8% (p-value = 0.077) decrease in NO2 levels for SP and RJ, respectively, in 2020 compared to 2019. For PM2.5, no significant change was observed for the same time period in the SP region, although the high number of fire burnings in the Southeast region seemed to be affecting PM2.5 levels. In addition to natural emissions (fire burnings), the combined data was also evaluated taking meteorological parameters, such as temperature and wind speed, into account. No interference of weather or fire was found in 2020 NO2 ground levels compared to previous years, This integrated analysis is innovative and has yet to be more explored in Brazilian studies. This is true specifically because the ground-based stations are spatially and temporally sparse in Brazil. / Master of Science / This study aims to explore satellite data applied to the lockdown context resultant from the COVID-19 pandemic in Brazil. Satellite data usage in air quality management is yet to be explored to its full potential. Two highly populated states were chosen: Sao Paulo (SP) and Rio de Janeiro (RJ). Local governments have been imposing limitations on private and public vehicle circulation, inducing a decrease in atmospheric pollutant levels, specifically nitrogen dioxide (NO2), which is directly emitted to the air by fuel combustion. NO2 is also short-lived in the atmosphere, so its variation within days can be easily captured. PM2.5, a category of fine inhalable particles, can be produced by wildfires, in addition to fuel burning and mechanical processes such as resuspension by cars. Here we retrieved daily NO2 vertical column densities for the month of May within the 2015-2020 years from the OMI instrument onboard of NASA's Aura satellite. Ground daily NO2 and PM2.5 measurements were also collected from local environmental agencies. Results showed an average 42% decrease of the NO2 column values in SP in 2020 compared to 2015-2019. The decrease was 49.6% in RJ for the same timeframe. Correspondent surface data showed a decrease of 13.3% (p-value = 0.099) and 18.8% (p-value = 0.077) during 2020 compared to 2019 in SP and RJ stations, respectively. No significant divergence in PM2.5 values was found between 2019 and 2020. Finally, weather data was added to the pollutant analysis. PM2.5 concentrations were associated with wildfires, while the NO2 levels found in 2020 for SP and RJ were attributed to local lockdown decrees. Satellite retrievals showed significant potential in filling out ground datasets, correlating with the SP and RJ surface data in 77% and 53%, respectively.

Exposure assessment is a critical step in air quality-related epidemiological studies. Accurate estimates of exposure within urban areas are a vital input to models that aim to assess the health effects of air quality among populations of interest. In this study, I have derived and applied a novel approach for capturing the distribution of air quality in Arlington, VA and Washington DC using mobile monitoring. The main objectives of this study are: 1. Deploy a year-long sampling campaign in the Washington DC region to capture the within-city variability of air quality for Particle Number Concentration (PNC), fine particulate matter (PM2.5), and Black Carbon (BC) using mobile monitoring. 2. Derive a method for selecting the best representative mobile monitoring routes to capture within-city spatial patterns of air quality. The end-use of the monitoring campaign described here is as an input for Land Use Regression (LUR) models. 3. Collect unconventional data to characterize the built environment, e.g., videos, sound, etc., that could be employed to improve the LUR models beyond conventional approaches. This study describes the data collection effort that was deployed for a year to characterize annual average concentrations at different locations across the Washington DC region. My thesis describes the challenges experienced and lessons learned during the data collection phase. The goal of this thesis is to describe the data collected and the methods used to sample the DC region. This effort is a component of a larger project that will later use these observations in LUR models. The central site used for measurement of background concentration had a lower concentration median when compared with the median concentration measured on bike. The median PM2.5 concentration at the central site was observed to be 5.2 μg/m3 and the median PNC at the central site was observed to be 6,365 #/cm3. The Arlington PM2.5 concentration was 1 μg/m3 and the Washington DC concentration PM2.5 was 0.3 μg/m3 higher than the background median concentration. Also, the Particle Number Concentration (PNC) was 222 #/cm3 more and the Washington DC PNC was 2,139 #/cm3 higher than the median background concentration. / Many studies have shown that living in polluted air has long-term negative impacts on human health. These negative impacts include premature death, lung disease, heart disease, blood disease, and other complications. Due to these impacts, it is critical to know the level of air pollution within cities to identify areas that have elevated concentrations. The measurement of air quality is challenging because of the low number of monitors available due to cost. Reference grade air quality monitors are often very costly. In this study, I have developed an approach for using a bike to collect mobile measurements of particulate air pollutants in the Washington DC area. I collected one year of data at a fixed site in Arlington and four seasons of data from the bike mobile monitoring campaign. After analyzing the data, I observed that the fixed station showed lower concentration when compared with the data collected by bicycle. I have also suggested improvements in the mobile monitoring method and developed an approach for joining these data with outputs from computer vision models to describe the built environment.

published_or_final_version / abstract / Mechanical Engineering / Master / Master of Philosophy

Fine particulate matter (PM2.5) has been associated with a variety of problems that include adverse health effects, reduction in visibility, damage to buildings and crops, and possible interactions with climate. Although stringent air quality regulations are in place, policy makers need efficient tools to test a wide range of control strategies. Sensitivity analysis provides predictions on how the interdependent concentrations of various PM2.5 components and also gaseous pollutant species will respond to specific combinations of precursor emission reductions. The Community Multiscale Air Quality Model (CMAQ) was outfitted with the Decoupled Direct Method in 3D for calculating sensitivities of particulate matter (DDM-3D/PM). This method was evaluated and applied to high PM2.5 episodes in the Southeast United States. Sensitivities of directly emitted particles as well as those formed in the atmosphere through chemical and physical processing of emissions of gaseous precursors such as SO2, NOx, VOCs, and NH3 were calculated. DDM-3D/PM was further extended to calculate receptor oriented sensitivities or the Area of Influence (AOI). AOI analysis determines the geographical extent of relative air pollutant precursor contributions to pollutant levels at a specific receptor of interest. This method was applied to Atlanta and other major cities in Georgia. The tools developed here (DDM-3D/PM and AOI) provide valuable information to those charged with air quality management.