GIS-based mapping and statistical analysis of air pollution and mortality in Brisbane, Australia

In this thesis, the relationship between air pollution and human health has been investigated utilising Geographic Information System (GIS) as an analysis tool. The research focused on how vehicular air pollution affects human health. The main objective of this study was to analyse the spatial variability of pollutants, taking Brisbane City in Australia as a case study, by the identification of the areas of high concentration of air pollutants and their relationship with the numbers of death caused by air pollutants. A correlation test was performed to establish the relationship between air pollution, number of deaths from respiratory disease, and total distance travelled by road vehicles in Brisbane. GIS was utilized to investigate the spatial distribution of the air pollutants. The main finding of this research is the comparison between spatial and non-spatial analysis approaches, which indicated that correlation analysis and simple buffer analysis of GIS using the average levels of air pollutants from a single monitoring station or by group of few monitoring stations is a relatively simple method for assessing the health effects of air pollution. There was a significant positive correlation between variable under consideration, and the research shows a decreasing trend of concentration of nitrogen dioxide at the Eagle Farm and Springwood sites and an increasing trend at CBD site. Statistical analysis shows that there exists a positive relationship between the level of emission and number of deaths, though the impact is not uniform as certain sections of the population are more vulnerable to exposure. Further statistical tests found that the elderly people of over 75 years age and children between 0-15 years of age are the more vulnerable people exposed to air pollution. A non-spatial approach alone may be insufficient for an appropriate evaluation of the impact of air pollutant variables and their inter-relationships. It is important to evaluate the spatial features of air pollutants before modeling the air pollution-health relationships.

Long-term effects of oil pollution in mangrove forests (Baixada Santista, Southeast Brazil) detected using a GIS-based multitemporal analysis of aerial photographs

Oil spills are potential threats to the integrity of highly productive coastal wetlands, such as mangrove forests. In October 1983, a mangrove area of nearly 300 ha located on the southeastern coast of Brazil was impacted by a 3.5 million liter crude oil spill released by a broken pipeline. In order to assess the long-term effects of oil pollution on mangrove vegetation, we carried out a GIS-based multitemporal analysis of aerial photographs of the years 1962, 1994, 2000 and 2003. Photointerpretation, visual classification, class quantification, ground-truth and vegetation structure data were combined to evaluate the oil impact. Before the spill, the mangroves exhibited a homogeneous canopy and well-developed stands. More than ten years after the spill, the mangrove vegetation exhibited three distinct zones reflecting the long-term effects of the oil pollution. The most impacted zone (10.5 ha) presented dead trees, exposed substrate and recovering stands with reduced structural development. We suggest that the distinct impact and recovery zones reflect the spatial variability of oil removal rates in the mangrove forest. This study identifies the multitemporal analysis of aerial photographs as a useful tool for assessing a system's capacity for recovery and monitoring the long-term residual effects of pollutants on vegetation dynamics, thus giving support to mangrove forest management and conservation.

Long-term effects of oil pollution in mangrove forests (Baixada Santista, Southeast Brazil) detected using a GIS-based multitemporal analysis of aerial photographs

Oil spills are potential threats to the integrity of highly productive coastal wetlands, such as mangrove forests. In October 1983, a mangrove area of nearly 300 ha located on the southeastern coast of Brazil was impacted by a 3.5 million liter crude oil spill released by a broken pipeline. In order to assess the long-term effects of oil pollution on mangrove vegetation, we carried out a GIS-based multitemporal analysis of aerial photographs of the years 1962, 1994, 2000 and 2003. Photointerpretation, visual classification, class quantification, ground-truth and vegetation structure data were combined to evaluate the oil impact. Before the spill, the mangroves exhibited a homogeneous canopy and well-developed stands. More than ten years after the spill, the mangrove vegetation exhibited three distinct zones reflecting the long-term effects of the oil pollution. The most impacted zone (10.5 ha) presented dead trees, exposed substrate and recovering stands with reduced structural development. We suggest that the distinct impact and recovery zones reflect the spatial variability of oil removal rates in the mangrove forest. This study identifies the multitemporal analysis of aerial photographs as a useful tool for assessing a system's capacity for recovery and monitoring the long-term residual effects of pollutants on vegetation dynamics, thus giving support to mangrove forest management and conservation.

Groundwater favorability map using GIS multicriteria data analysis on crystalline terrain, Sao Paulo State, Brazil

This paper presents the groundwater favorability mapping on a fractured terrain in the eastern portion of Sao Paulo State, Brazil. Remote sensing, airborne geophysical data, photogeologic interpretation, geologic and geomorphologic maps and geographic information system (GIS) techniques have been used. The results of cross-tabulation between these maps and well yield data allowed groundwater prospective parameters in a fractured-bedrock aquifer. These prospective parameters are the base for the favorability analysis whose principle is based on the knowledge-driven method. The mutticriteria analysis (weighted linear combination) was carried out to give a groundwater favorabitity map, because the prospective parameters have different weights of importance and different classes of each parameter. The groundwater favorability map was tested by cross-tabulation with new well yield data and spring occurrence. The wells with the highest values of productivity, as well as all the springs occurrence are situated in the excellent and good favorabitity mapped areas. It shows good coherence between the prospective parameters and the well yield and the importance of GIS techniques for definition of target areas for detail study and wells location. (c) 2008 Elsevier B.V. All rights reserved.

Spatial analysis of cardiovascular disease mortality and air pollution in Harris County, Texas

The association between fine particulate matter air pollution (PM2.5) and cardiovascular disease (CVD) mortality was spatially analyzed for Harris County, Texas, at the census tract level. The objective was to assess how increased PM2.5 exposure related to CVD mortality in this area while controlling for race, income, education, and age. An estimated exposure raster was created for Harris County using Kriging to estimate the PM2.5 exposure at the census tract level. The PM2.5 exposure and the CVD mortality rates were analyzed in an Ordinary Least Squares (OLS) regression model and the residuals were subsequently assessed for spatial autocorrelation. Race, median household income, and age were all found to be significant (p<0.05) predictors in the model. This study found that for every one μg/m3 increase in PM2.5 exposure, holding age and education variables constant, an increase of 16.57 CVD deaths per 100,000 would be predicted for increased minimum exposure values and an increase of 14.47 CVD deaths per 100,000 would be predicted for increased maximum exposure values. This finding supports previous studies associating PM2.5 exposure with CVD mortality. This study further identified the areas of greatest PM2.5 exposure in Harris County as being the geographical locations of populations with the highest risk of CVD (i.e., predominantly older, low-income populations with a predominance of African Americans). The magnitude of the effect of PM2.5 exposure on CVD mortality rates in the study region indicates a need for further community-level studies in Harris County, and suggests that reducing excess PM2.5 exposure would reduce CVD mortality.^

GIS-Based Suitability Analysis and Planning of Green Infrastructure: A Case of the PPCOD, Capitol Hill

Thesis (Master's)--University of Washington, 2016-06

Fine-scale spatial genetic correlation analyses reveal strong female philopatry within a brush-tailed rock-wallaby colony in southeast Queensland

We combine spatial data on home ranges of individuals and microsatellite markers to examine patterns of fine-scale spatial genetic structure and dispersal within a brush-tailed rock-wallaby (Petrogale penicillata) colony at Hurdle Creek Valley, Queensland. Brush-tailed rock-wallabies were once abundant and widespread throughout the rocky terrain of southeastern Australia; however, populations are nearly extinct in the south of their range and in decline elsewhere. We use pairwise relatedness measures and a recent multilocus spatial autocorrelation analysis to test the hypotheses that in this species, within-colony dispersal is male-biased and that female philopatry results in spatial clusters of related females within the colony. We provide clear evidence for strong female philopatry and male-biased dispersal within this rock-wallaby colony. There was a strong, significant negative correlation between pairwise relatedness and geographical distance of individual females along only 800 m of cliff line. Spatial genetic autocorrelation analyses showed significant positive correlation for females in close proximity to each other and revealed a genetic neighbourhood size of only 600 m for females. Our study is the first to report on the fine-scale spatial genetic structure within a rock-wallaby colony and we provide the first robust evidence for strong female philopatry and spatial clustering of related females within this taxon. We discuss the ecological and conservation implications of our findings for rock-wallabies, as well as the importance of fine-scale spatial genetic patterns in studies of dispersal behaviour.

Particulate air pollution and cardiorespiratory hospital admissions in a temperate Australian city: A case-crossover analysis

Although ambient air pollution exposure has been linked with poor health in many parts of the world, no previous study has investigated the effect on morbidity in the city of Adelaide, South Australia. To explore the association between particulate matter (PM) and hospitalisations, including respiratory and cardiovascular admissions in Adelaide, South Australia. Methods: For the study period September 2001 to October 2007, daily counts of all-cause, cardiovascular and respiratory hospital admissions were collected, as well as daily air quality data including concentrations of particulates, ozone and nitrogen dioxide. Visibility codes for presentweather conditions identified dayswhen airborne dust or smoke was observed. The associations between PM and hospitalisations were estimated using timestratified case-crossover analyses controlling for covariates including temperature, relative humidity, other pollutants, day of the week and public holidays. Mean PM10 concentrations were higher in the warm season, whereas PM2.5 concentrations were higher in the cool season. Hospital admissions were associated with PM10 in the cool season and with PM2.5 in both seasons. No significant effect of PM on all-age respiratory admissions was detected, however cardiovascular admissions were associated with both PM2.5 and PM10 in the cool season with the highest effects for PM2.5 (4.48%, 95% CI: 0.74%, 8.36% increase per 10 μg/m3 increase in PM2.5). These findings suggest that despite the city's relatively low levels of air pollution, PMconcentrations are associated with increases in morbidity in Adelaide. Further studies are needed to investigate the sources of PM which may be contributing to the higher cool season effects.

Decomposition analysis of air pollution abatement in China: empirical study for ten industrial sectors from 1998 to 2009

This study analyzes the management of air pollutant substance in Chinese industrial sectors from 1998 to 2009. Decomposition analysis applying the logarithmic mean divisia index is used to analyze changes in emissions of air pollutants with a focus on the following five factors: coal pollution intensity (CPI), end-of-pipe treatment (EOP), the energy mix (EM), productive efficiency change (EFF), and production scale changes (PSC). Three pollutants are the main focus of this study: sulfur dioxide (SO2), dust, and soot. The novelty of this paper is focusing on the impact of the elimination policy on air pollution management in China by type of industry using the scale merit effect for pollution abatement technology change. First, the increase in SO2 emissions from Chinese industrial sectors because of the increase in the production scale is demonstrated. However, the EOP equipment that induced this change and improvements in energy efficiency has prevented an increase in SO2 emissions that is commensurate with the increase in production. Second, soot emissions were successfully reduced and controlled in all industries except the steel industry between 1998 and 2009, even though the production scale expanded for these industries. This reduction was achieved through improvements in EOP technology and in energy efficiency. Dust emissions decreased by nearly 65% between 1998 and 2009 in the Chinese industrial sectors. This successful reduction in emissions was achieved by implementing EOP technology and pollution prevention activities during the production processes, especially in the cement industry. Finally, pollution prevention in the cement industry is shown to result from production technology development rather than scale merit. © 2013 Elsevier Ltd. All rights reserved.

Local air pollution and global climate change taxes: a distributional analysis

25 p.

Case-Crossover Analysis of Air Pollution Health Effects: A Systematic Review of Methodology and Application

Case-crossover is one of the most used designs for analyzing the health-related effects of air pollution. Nevertheless, no one has reviewed its application and methodology in this context. Objective: We conducted a systematic review of case-crossover (CCO) designs used to study the relationship between air pollution and morbidity and mortality, from the standpoint of methodology and application.Data sources and extraction: A search was made of the MEDLINE and EMBASE databases.Reports were classified as methodologic or applied. From the latter, the following information was extracted: author, study location, year, type of population (general or patients), dependent variable(s), independent variable(s), type of CCO design, and whether effect modification was analyzed for variables at the individual level. Data synthesis: The review covered 105 reports that fulfilled the inclusion criteria. Of these, 24 addressed methodological aspects, and the remainder involved the design’s application. In the methodological reports, the designs that yielded the best results in simulation were symmetric bidirectional CCO and time-stratified CCO. Furthermore, we observed an increase across time in the use of certain CCO designs, mainly symmetric bidirectional and time-stratified CCO. The dependent variables most frequently analyzed were those relating to hospital morbidity; the pollutants most often studied were those linked to particulate matter. Among the CCO-application reports, 13.6% studied effect modification for variables at the individual level.Conclusions: The use of CCO designs has undergone considerable growth; the most widely used designs were those that yielded better results in simulation studies: symmetric bidirectional and time-stratified CCO. However, the advantages of CCO as a method of analysis of variables at the individual level are put to little use

Comparing meta-analysis and ecological-longitudinal analysis in time-series studies: a case study of the effects of air pollution on mortality in three Spanish cities

The objective of this paper is to introduce a diVerent approach, called the ecological-longitudinal, to carrying out pooled analysis in time series ecological studies. Because it gives a larger number of data points and, hence, increases the statistical power of the analysis, this approach, unlike conventional ones, allows the complementation of aspects such as accommodation of random effect models, of lags, of interaction between pollutants and between pollutants and meteorological variables, that are hardly implemented in conventional approaches. Design—The approach is illustrated by providing quantitative estimates of the short-termeVects of air pollution on mortality in three Spanish cities, Barcelona,Valencia and Vigo, for the period 1992–1994. Because the dependent variable was a count, a Poisson generalised linear model was first specified. Several modelling issues are worth mentioning. Firstly, because the relations between mortality and explanatory variables were nonlinear, cubic splines were used for covariate control, leading to a generalised additive model, GAM. Secondly, the effects of the predictors on the response were allowed to occur with some lag. Thirdly, the residual autocorrelation, because of imperfect control, was controlled for by means of an autoregressive Poisson GAM. Finally, the longitudinal design demanded the consideration of the existence of individual heterogeneity, requiring the consideration of mixed models. Main results—The estimates of the relative risks obtained from the individual analyses varied across cities, particularly those associated with sulphur dioxide. The highest relative risks corresponded to black smoke in Valencia. These estimates were higher than those obtained from the ecological-longitudinal analysis. Relative risks estimated from this latter analysis were practically identical across cities, 1.00638 (95% confidence intervals 1.0002, 1.0011) for a black smoke increase of 10 μg/m3 and 1.00415 (95% CI 1.0001, 1.0007) for a increase of 10 μg/m3 of sulphur dioxide. Because the statistical power is higher than in the individual analysis more interactions were statistically significant,especially those among air pollutants and meteorological variables. Conclusions—Air pollutant levels were related to mortality in the three cities of the study, Barcelona, Valencia and Vigo. These results were consistent with similar studies in other cities, with other multicentric studies and coherent with both, previous individual, for each city, and multicentric studies for all three cities

Applications of redundancy analysis for the detection of chemical response patterns to air pollution in lichen

The lichens Ramalina celastri (Spreng.) Krog & Swinsc., Punctelia microsticta (Müll. Arg.) Krog and Canomaculina pilosa (Stizenb.) Elix & Hale were transplanted simultaneously to 17 urban-industrial sites in a northwestern area of Córdoba city, Argentina. The transplantation sites were set according to different environmental conditions: traffic, industries, tree cover, building height, topographic level, position in the block and distances from the river and from the power plant. Three months later, chlorophyll a, chlorophyll b, phaeophytin a, soluble proteins, hydroperoxy conjugated dienes, malondialdehyde concentration and sulfur accumulation were determined, and a pollution index was calculated for each sampling site. Redundancy analysis was applied to detect the variation pattern of the lichen variables that can be 'best' explained by the environmental variables considered. The present study provides information about both the specific pattern response of each species to atmospheric pollution, and environmental conditions that determine it. As regards pollutants emission sources R. celastri showed a chemical response associated mainly with pollutant released by the power plant and traffic. P. microsticta and C. pilosa responded mainly to industrial sources. Regarding environmental conditions that affect the spreading of air pollutants and their incidence on the bioindicator, the topographic level and tree cover surrounding the sampling site were found to be important for R. celastri, tree cover surrounding the sampling site and the building height affected P. microsticta, while building height did so for C. pilosa.