LUR models for particulate matters in the Taipei metropolis with high densities of roads and strong activities of industry, commerce and construction

Lee, Jui-Huna; Wu, Chang‐Fu; Hoek, Gerard; Hoogh, Kees de; Beelen, Rob; Brunekreef, Bert; Chan, Chang-Chuan

doi:10.1016/j.scitotenv.2015.01.091

Cited by 55 publications

(39 citation statements)

References 19 publications

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“…The LUR model has been applied in more than nine countries and 14 cities across Europe (e.g., [42,43]) and North America (e.g., [44,45]). European and North American LUR models yielded a predictive capacity (as R 2 ) ranging from 35% to 94% for PM 2.5 [36].…”

Section: Lur Modelmentioning

confidence: 99%

“…Moreover, the interpolation method is only based on the monitoring of data, which means that it is hard to indicate the spatial variation of pollutant concentration on a small scale [35]. Compared with these methods, land-use regression (LUR) has been widely used, and has rapidly become an important approach for predicting long-term average pollutant concentration at an intra-urban scale [36]. Also, it is a promising approach for predicting ambient air pollutant concentrations at high spatial resolution [37,38], as it has a low requirement for the categories of the data, and the model is simple to construct.…”

Section: Lur Modelmentioning

confidence: 99%

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Influence of Road Patterns on PM2.5 Concentrations and the Available Solutions: The Case of Beijing City, China

2017

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Abstract:With the increase in urbanization and energy consumption, PM 2.5 has become a major pollutant. This paper investigates the impact of road patterns on PM 2.5 pollution in Beijing, focusing on two questions: Do road patterns significantly affect PM 2.5 concentrations? How do road patterns affect PM 2.5 concentrations? A land-use regression model (LUR model) is used to quantify the associations between PM 2.5 concentrations, and road patterns, land-use patterns, and population density. Then, in the condition of excluding other factors closely correlated to PM 2.5 concentrations, based on the results of the regression model, further research is conducted to explore the relationship between PM 2.5 concentrations and the types, densities, and layouts of road networks, through the controlling variables method. The results are as follows: (1) the regression coefficient of road patterns is significantly higher than the water area, population density, and transport facilities, indicating that road patterns have an obvious influence on PM 2.5 concentrations; (2) under the same traffic carrying capacity, the layout of "a tight network of streets and small blocks" is superior to that of "a sparse network of streets and big blocks"; (3) the grade proportion of urban roads impacts the road patterns' rationality, and a high percentage of branch roads and secondary roads could decrease PM 2.5 concentrations. These findings could provide a reference for the improvement of the traffic structure and air quality of Beijing.

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Section: Lur Modelmentioning

confidence: 99%

Section: Lur Modelmentioning

confidence: 99%

Influence of Road Patterns on PM2.5 Concentrations and the Available Solutions: The Case of Beijing City, China

2017

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“…In addition, the Cook's distance, which is a measure to check for outliers and influential variables, was also used in model evaluation [44,45]. Spatial autocorrelation using Moran's I statistic was checked both for average noise levels [20] in sample data at the three temporal resolutions and residuals of all best models [20,[44][45][46].…”

Section: Overview Of Experiments Designmentioning

confidence: 99%

Modelling of intra-urban variability of prevailing ambient noise at different temporal resolution

et al. 2017

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Abstract:The impact of temporal aspects of noise data on model development and intra-urban variability on environmental noise levels are often ignored in the development of models used to predict its spatiotemporal variation within a city. Using a Land Use Regression approach, this study develops a framework which uses routine noise monitors to model the prevailing ambient noise, and to develop a noise variability map showing the variation within a city caused by land-use setting. The impact of data resolution on model development and the impact of meteorological variables on the noise level which are often ignored were also assessed. Six models were developed based on monthly, daily and hourly resolutions of both the noise and predictor data. Cross validation highlighted that only the hourly resolution model having 59% explanatory power of the observed data (adjusted R 2 ) and a potential of explaining at least 0.47% variation of any independent dataset (cross validation R 2 ), was a suitable candidate among all the developed models for explaining intraurban variability of noise. In the hourly model, regions with roads of high traffic volumes, with higher concentrations of heavy goods vehicles, and being close to activity centres were found to have more impact on the prevailing ambient noise. Road lengths were found to be the most influential predictors and identified as having an impact on the ambient noise monitors.

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“…In addition, the SPER is low when estimating PM 2.5 in Beijing and results in some negative concentration estimations, which is not consistent with the fact that the minimum PM 2.5 concentration should be zero [41]. In some recent studies, attempts were made to improve the accuracy of PM 2.5 estimates by using the following two approaches: (1) adding more predictor variables, e.g., on-road mobile emissions and stationary emissions data were added to LUR models in [27], satellite data were added in [33], and industry, commerce, and construction activities were added in [42]; and (2) combining LUR models with other models, e.g., a dispersion model in [36], and the Bayesian maximum entropy method in [38]. The first approach has more restrictions because the use of different regions in different countries results in different types of variables.…”

Section: Introductionmentioning

confidence: 99%

Self-Adaptive Revised Land Use Regression Models for Estimating PM2.5 Concentrations in Beijing, China

Ji-ping

Zongyi

2016

Sustainability

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Heavy air pollution, especially fine particulate matter (PM 2.5 ), poses serious challenges to environmental sustainability in Beijing. Epidemiological studies and the identification of measures for preventing serious air pollution both require accurate PM 2.5 spatial distribution data. Land use regression (LUR) models are promising for estimating the spatial distribution of PM 2.5 at a high spatial resolution. However, typical LUR models have a limited sampling point explanation rate (SPER, i.e., the rate of the sampling points with reasonable predicted concentrations to the total number of sampling points) and accuracy. Hence, self-adaptive revised LUR models are proposed in this paper for improving the SPER and accuracy of typical LUR models. The self-adaptive revised LUR model combines a typical LUR model with self-adaptive LUR model groups. The typical LUR model was used to estimate the PM 2.5 concentrations, and the self-adaptive LUR model groups were constructed for all of the sampling points removed from the typical LUR model because they were beyond the prediction data range, which was from 60% of the minimum observation to 120% of the maximum observation. The final results were analyzed using three methods, including an accuracy analysis, and were compared with typical LUR model results and the spatial variations in Beijing. The accuracy satisfied the demands of the analysis, and the accuracies at the different monitoring sites indicated spatial variations in the accuracy of the self-adaptive revised LUR model. The accuracy was high in the central area and low in suburban areas. The comparison analysis showed that the self-adaptive LUR model increased the SPER from 75% to 90% and increased the accuracy (based on the root-mean-square error) from 20.643 µg/m 3 to 17.443 µg/m 3 for the PM 2.5 concentrations during the winter of 2014 in Beijing. The spatial variation analysis for Beijing showed that the PM 2.5 concentrations were low in the north, especially in the northwest region, and high in the southern and central portions of Beijing. This spatial variation was consistent with the fact that the northern region is mountainous and has fewer people and less traffic, which results in lower air pollution, than in the central region, which has a high population density and heavy traffic. Moreover, the southern region is adjacent to Hebei province, which contains many polluting enterprises; thus, this area exhibits higher air pollution levels than Beijing. Therefore, the self-adaptive revised LUR model is effective and reliable.

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LUR models for particulate matters in the Taipei metropolis with high densities of roads and strong activities of industry, commerce and construction

Cited by 55 publications

References 19 publications

Influence of Road Patterns on PM2.5 Concentrations and the Available Solutions: The Case of Beijing City, China

Influence of Road Patterns on PM2.5 Concentrations and the Available Solutions: The Case of Beijing City, China

Modelling of intra-urban variability of prevailing ambient noise at different temporal resolution

Self-Adaptive Revised Land Use Regression Models for Estimating PM2.5 Concentrations in Beijing, China

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