Effects of Spatial Structure on Air Quality Level in U.S. Metropolitan Areas

The purpose of this dissertation is to investigate relationships between metropolitan spatial structure and air quality across U.S. metropolitan areas. Debates over compact city and sprawling development models as alternative patterns of metropolitan development and planning remain unsettled. This dissertation works from the hypothesis that compact regions with high-density, concentration, mixed land use, and better accessibility improve air quality. To test the compact city hypothesis, this dissertation uses a combined spatial data of population, employment, government, land use, and air quality in 610 counties in U.S. metropolitan areas and their neighboring areas for 1990, 2000, and 2006. Indicators identified widely in literature are employed to measure compact city: land uses, density, concentration, accessibility, and centralization. This dissertation provides the empirical evidence on the basis of some stipulated causal relationships between compact regions and air quality through multivariate regression models using spatial econometric analysis, that sheds light on the presence of spatial dependence between spatial variations in alternative spatial structures and changes in air quality level. The empirical results show a number of interesting signs to the compact city hypothesis. Metropolitan areas with a higher percentage of developed open space or longer weighted average daily commute time bring out higher average air quality index values, leading to worsened air quality. On the contrary, metropolitan areas with a higher percentage of densely employed sub-areas produce lower average air quality index values, resulting in improved air quality. The empirical findings contribute to the importance of compact development strategies, such as polycentric employment centers, on improved air quality over suburban sprawl in the United States towards successful sustainable metropolitan development and plannin

Effects of Spatial Structure on Air Quality Level in U.S. Metropolitan Areas

The purpose of this dissertation is to investigate relationships between metropolitan spatial structure and air quality across U.S. metropolitan areas. Debates over compact city and sprawling development models as alternative patterns of metropolitan development and planning remain unsettled. This dissertation works from the hypothesis that compact regions with high-density, concentration, mixed land use, and better accessibility improve air quality. To test the compact city hypothesis, this dissertation uses a combined spatial data of population, employment, government, land use, and air quality in 610 counties in U.S. metropolitan areas and their neighboring areas for 1990, 2000, and 2006. Indicators identified widely in literature are employed to measure compact city: land uses, density, concentration, accessibility, and centralization. This dissertation provides the empirical evidence on the basis of some stipulated causal relationships between compact regions and air quality through multivariate regression models using spatial econometric analysis, that sheds light on the presence of spatial dependence between spatial variations in alternative spatial structures and changes in air quality level. The empirical results show a number of interesting signs to the compact city hypothesis. Metropolitan areas with a higher percentage of developed open space or longer weighted average daily commute time bring out higher average air quality index values, leading to worsened air quality. On the contrary, metropolitan areas with a higher percentage of densely employed sub-areas produce lower average air quality index values, resulting in improved air quality. The empirical findings contribute to the importance of compact development strategies, such as polycentric employment centers, on improved air quality over suburban sprawl in the United States towards successful sustainable metropolitan development and plannin

Effects of Spatial Structure on Air Quality Level in U.S. Metropolitan Areas

The purpose of this dissertation is to investigate relationships between metropolitan spatial structure and air quality across U.S. metropolitan areas. Debates over compact city and sprawling development models as alternative patterns of metropolitan development and planning remain unsettled. This dissertation works from the hypothesis that compact regions with high-density, concentration, mixed land use, and better accessibility improve air quality. To test the compact city hypothesis, this dissertation uses a combined spatial data of population, employment, government, land use, and air quality in 610 counties in U.S. metropolitan areas and their neighboring areas for 1990, 2000, and 2006. Indicators identified widely in literature are employed to measure compact city: land uses, density, concentration, accessibility, and centralization. This dissertation provides the empirical evidence on the basis of some stipulated causal relationships between compact regions and air quality through multivariate regression models using spatial econometric analysis, that sheds light on the presence of spatial dependence between spatial variations in alternative spatial structures and changes in air quality level. The empirical results show a number of interesting signs to the compact city hypothesis. Metropolitan areas with a higher percentage of developed open space or longer weighted average daily commute time bring out higher average air quality index values, leading to worsened air quality. On the contrary, metropolitan areas with a higher percentage of densely employed sub-areas produce lower average air quality index values, resulting in improved air quality. The empirical findings contribute to the importance of compact development strategies, such as polycentric employment centers, on improved air quality over suburban sprawl in the United States towards successful sustainable metropolitan development and plannin

Modelling collinear and spatially correlated data

In this work we present a statistical approach to distinguish and interpret the complex relationship between several predictors and a response variable at the small area level, in the presence of i) high correlation between the predictors and ii) spatial correlation for the response. Covariates which are highly correlated create collinearity problems when used in a standard multiple regression model. Many methods have been proposed in the literature to address this issue. A very common approach is to create an index which aggregates all the highly correlated variables of interest. For example, it is well known that there is a relationship between social deprivation measured through the Multiple Deprivation Index (IMD) and air pollution; this index is then used as a confounder in assessing the e ect of air pollution on health outcomes (e.g. respiratory hospital admissions or mortality). However it would be more informative to look specically at each domain of the IMD and at its relationship with air pollution to better understand its role as a confounder in the epidemiological analyses. In this paper we illustrate how the complex relationships between the domains of IMD and air pollution can be deconstructed and analysed using pro le regression, a Bayesian non-parametric model for clustering responses and covariates simultaneously. Moreover, we include an intrinsic spatial conditional autoregressive (ICAR) term to account for the spatial correlation of the response variable

DEFINITION OF NOVEL HEALTH AND AIR POLLUTION INDEX BASED ON SHORT TERM EXPOSURE AND AIR CONCENTRATION LEVELS

Health impact assessment has become important in the development of air quality policies and in finding the relationships between pollutants concentration and health effects. In our work we presented a novel index able to evaluate the effects on the human exposure caused by ambient air pollution in urban areas. The index is able to link both health risk factors and pollutants levels. The indexes is of additive type and is composed by two terms: the former is based on pollutants concentration and is connected with EPA air quality index (AQI), while the latter is composed by an adimensional term based on the exposure levels. We tested the methodology using PM10 as studied pollutants. The spatial and temporal variation of its health impact was evaluated by means of index maps applying the above methodology in the city of Rome during three selected episodes. Our study shows index maps for all episodes linked to population and to pollutants

A psychophysical measurement on subjective well-being and air pollution

Although the physical effects of air pollution on humans are well documented, there may be even greater impacts on the emotional state and health. Surveys have traditionally been used to explore the impact of air pollution on people’s subjective well-being (SWB). However, the survey techniques usually take long periods to properly match the air pollution characteristics from monitoring stations to each respondent’s SWB at both disaggregated spatial and temporal levels. Here, we used air pollution data to simulate fixed-scene images and psychophysical process to examine the impact from only air pollution on SWB. Findings suggest that under the atmospheric conditions in Beijing, negative emotions occur when PM2.5 (particulate matter with a diameter less than 2.5 µm) increases to approximately 150 AQI (air quality index). The British observers have a stronger negative response under severe air pollution compared with Chinese observers. People from different social groups appear to have different sensitivities to SWB when air quality index exceeds approximately 200 AQI

How robust are the estimated effects of air pollution on health? Accounting for model uncertainty using Bayesian model averaging

The long-term impact of air pollution on human health can be estimated from small-area ecological studies in which the health outcome is regressed against air pollution concentrations and other covariates, such as socio-economic deprivation. Socio-economic deprivation is multi-factorial and difficult to measure, and includes aspects of income, education, and housing as well as others. However, these variables are potentially highly correlated, meaning one can either create an overall deprivation index, or use the individual characteristics, which can result in a variety of pollution-health effects. Other aspects of model choice may affect the pollution-health estimate, such as the estimation of pollution, and spatial autocorrelation model. Therefore, we propose a Bayesian model averaging approach to combine the results from multiple statistical models to produce a more robust representation of the overall pollution-health effect. We investigate the relationship between nitrogen dioxide concentrations and cardio-respiratory mortality in West Central Scotland between 2006 and 2012

CSWA: Aggregation-Free Spatial-Temporal Community Sensing

In this paper, we present a novel community sensing paradigm -- {C}ommunity {S}ensing {W}ithout {A}ggregation}. CSWA is designed to obtain the environment information (e.g., air pollution or temperature) in each subarea of the target area, without aggregating sensor and location data collected by community members. CSWA operates on top of a secured peer-to-peer network over the community members and proposes a novel \emph{Decentralized Spatial-Temporal Compressive Sensing} framework based on \emph{Parallelized Stochastic Gradient Descent}. Through learning the \emph{low-rank structure} via distributed optimization, CSWA approximates the value of the sensor data in each subarea (both covered and uncovered) for each sensing cycle using the sensor data locally stored in each member's mobile device. Simulation experiments based on real-world datasets demonstrate that CSWA exhibits low approximation error (i.e., less than $0.2 ^\circ$C in city-wide temperature sensing task and $10$ units of PM2.5 index in urban air pollution sensing) and performs comparably to (sometimes better than) state-of-the-art algorithms based on the data aggregation and centralized computation.Comment: This paper has been accepted by AAAI 2018. First two authors are equally contribute

The role of heat-flux–temperature covariance in the evolution of weather systems

Local diabatic heating and temperature anomaly fields need to be positively correlated for the diabatic heating to maintain a circulation against dissipation. Here we quantify the thermodynamic contribution of local air–sea heat exchange on the evolution of weather systems using an index of the spatial covariance between heat flux at the air–sea interface and air temperature at 850 hPa upstream of the North Atlantic storm track, corresponding with the Gulf Stream extension region. The index is found to be almost exclusively negative, indicating that the air–sea heat fluxes act locally as a sink on potential energy. It features bursts of high activity alternating with longer periods of lower activity. The characteristics of these high-index bursts are elucidated through composite analysis and the mechanisms are investigated in a phase space spanned by two different index components. It is found that the negative peaks in the index correspond with thermodynamic activity triggered by the passage of a weather system over a spatially variable sea-surface temperature field; our results indicate that most of this thermodynamically active heat exchange is realised within the cold sector of the weather systems

Spatio-temporal Variability in Surface Ocean pCO2 Inferred from Observations

The variability of surface ocean pCO2 is examined on multiple spatial and temporal scales. Temporal autocorrelation analysis is used to examine pCO2 variability over multiple years. Spatial autocorrelation analysis describes pCO2 variability over multiple spatial scales. Spatial autocorrelation lengths range between <50 km in coastal regions and other areas of physical turbulence up to 3,000 km along major currents. Analysis of the drivers of pCO2 shows that ocean currents are the primary driver of spatial variability. Autocorrelation lengths of air-sea CO2 fluxes are approximately half as long as for pCO2 due to the effects of highly variable wind speeds. The influence of modes of climate variability on ocean pCO2 and related air-sea CO2 fluxes is examined through correlations of climate indices with interannual pCO2 anomalies separated from the long-term trend and mean seasonal cycle. Changes in the El Ni˜no Southern Oscillation alter pCO2 levels by -6.6 � 1.0 �atm per index unit (�atm i