Comparison of model estimates from an intra-city land use regression model with a national satellite-LUR and a regional Bayesian Maximum Entropy model, in estimating NO2 for a birth cohort in Sydney, Australia

Cowie, Christine; Garden, Frances L.; Jegasothy, Edward; Knibbs, Luke D.; Hanigan, Ivan; Morley, David; Hansell, Anna; Hoek, Gerard; Marks, Guy B.

doi:10.1016/j.envres.2019.03.068

Cited by 29 publications

(25 citation statements)

References 55 publications

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“…Although Sydney's most extreme pollution events are often associated with fires and dust storms (e.g., [28,[50][51][52]), studies analysing the sources of PM 2.5 and NO X in NSW concluded that motor vehicle exhaust was the largest contributor to total NO X [53] and the largest anthropogenic contributor to total PM 2.5 [54]. Power stations are also implicated as a major source of atmospheric fine particles [18].…”

Section: Major Anthropogenic Sources Of Air Pollutionmentioning

confidence: 99%

“…Vehicle-related pollution levels have high spatial variability and are influenced by many factors, including wind speed, traffic density, and distance from main roads [55]. Many studies have shown that pollution levels decrease rapidly with distance from major roads [53,[55][56][57][58][59][60][61]. There is now widespread evidence of the adverse health effects associated with exposure to traffic-related air pollution [62][63][64], including studies finding associations between health risks, such as the increased risk of asthma in children living closer to major roads [65].…”

Section: Major Anthropogenic Sources Of Air Pollutionmentioning

confidence: 99%

“…Such modelling comparisons help cross-validate the models, test their skill at reproducing observed atmospheric composition, and identify any flaws or problems in the way that the models are set up or run [53,147]. At the end of the comparison exercise, the validated models may be used to undertake a number of different studies with added confidence in the modelled output [143,148,149].…”

Section: Caul Air Quality Modelling Comparison and Modelling Studiesmentioning

confidence: 99%

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A Clean Air Plan for Sydney: An Overview of the Special Issue on Air Quality in New South Wales

et al. 2019

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This paper presents a summary of the key findings of the special issue of Atmosphere on Air Quality in New South Wales and discusses the implications of the work for policy makers and individuals. This special edition presents new air quality research in Australia undertaken by (or in association with) the Clean Air and Urban Landscapes hub, which is funded by the National Environmental Science Program on behalf of the Australian Government’s Department of the Environment and Energy. Air pollution in Australian cities is generally low, with typical concentrations of key pollutants at much lower levels than experienced in comparable cities in many other parts of the world. Australian cities do experience occasional exceedances in ozone and PM2.5 (above air pollution guidelines), as well as extreme pollution events, often as a result of bushfires, dust storms, or heatwaves. Even in the absence of extreme events, natural emissions play a significant role in influencing the Australian urban environment, due to the remoteness from large regional anthropogenic emission sources. By studying air quality in Australia, we can gain a greater understanding of the underlying atmospheric chemistry and health risks in less polluted atmospheric environments, and the health benefits of continued reduction in air pollution. These conditions may be representative of future air quality scenarios for parts of the Northern Hemisphere, as legislation and cleaner technologies reduce anthropogenic air pollution in European, American, and Asian cities. However, in many instances, current legislation regarding emissions in Australia is significantly more lax than in other developed countries, making Australia vulnerable to worsening air pollution in association with future population growth. The need to avoid complacency is highlighted by recent epidemiological research, reporting associations between air pollution and adverse health outcomes even at air pollutant concentrations that are lower than Australia’s national air quality standards. Improving air quality is expected to improve health outcomes at any pollution level, with specific benefits projected for reductions in long-term exposure to average PM2.5 concentrations.

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Section: Major Anthropogenic Sources Of Air Pollutionmentioning

confidence: 99%

Section: Major Anthropogenic Sources Of Air Pollutionmentioning

confidence: 99%

Section: Caul Air Quality Modelling Comparison and Modelling Studiesmentioning

confidence: 99%

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A Clean Air Plan for Sydney: An Overview of the Special Issue on Air Quality in New South Wales

et al. 2019

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“…Although there is human activity in these non-urban districts, it is much less intensive than that in urban areas. LUR modeling predicts air pollutant concentrations based on surrounding land use and environmental properties [24][25][26]38,39]. To develop this model, we used spatialized environmental databases as predictor variables.…”

Section: Prediction Of No 2 Concentrations By Administrative Unit Usimentioning

confidence: 99%

“…Based on this data, an accurate NO 2 concentration map for administrative units was constructed, and seasonal concentrations were predicted using land use regression analysis. The LUR model is a statistical method that is widely used for analyzing the characteristics of air pollutant concentrations, because it has the advantage of being able to grasp the environmental influence on the NO 2 concentration spatially [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Mapping and Statistical Analysis of NO2 Concentration for Local Government Air Quality Regulation

2019

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With the growing interest in healthy living worldwide, there has been an increasing demand for more accurate measurements of the concentrations of air pollutants such as NO2. In particular, analyzing the characteristics and sources of air pollutants by region could improve the effectiveness of environmental policies applied in accordance with the environmental characteristics of individual regions. In this study, a detailed nationwide NO2 concentration map was generated using the cokriging interpolation technique, which integrates ground observations and satellite image data. The root-mean-square standardized (RMSS) error for this technique was close to 1, which indicates high accuracy. Using spatially interpolated NO2 concentration data, an administrative unit map was generated. When comparing the data for four NO2 data sources (observation data, satellite image data, detailed national data interpolated using cokriging, and NO2 concentrations averaged by an administrative unit based on the interpolated NO2 concentration data), the average concentrations were highest for remote sensing data. Land use regression (LUR) models of urban and non-urban regions were then developed to analyze the characteristics of the NO2 concentration by region using NO2 concentrations for the administrative units.

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An Implementation of Formal Framework for Collective Systems in Air Pollution Prediction System

Palak

Wojtkiewicz

Merayo

2021

Computational Collective Intelligence

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Comparison of model estimates from an intra-city land use regression model with a national satellite-LUR and a regional Bayesian Maximum Entropy model, in estimating NO2 for a birth cohort in Sydney, Australia

Abstract: Comparison of model estimates from an intra-city land use regression model with a national satellite-LUR and a regional Bayesian Maximum Entropy model, in estimating NO 2 for a birth cohort in Sydney, Australia

Cited by 29 publications

References 55 publications

A Clean Air Plan for Sydney: An Overview of the Special Issue on Air Quality in New South Wales

A Clean Air Plan for Sydney: An Overview of the Special Issue on Air Quality in New South Wales

Mapping and Statistical Analysis of NO2 Concentration for Local Government Air Quality Regulation

An Implementation of Formal Framework for Collective Systems in Air Pollution Prediction System

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