Land use regression models for estimating individual NOx and NO2 exposures in a metropolis with a high density of traffic roads and population

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

doi:10.1016/j.scitotenv.2013.11.064

Cited by 108 publications

(77 citation statements)

References 21 publications

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“…A previous study ) conducted in six downtown districts in autumn, 2012 produced a similar result (102.5 μg/m 3 ) to our model, but the kriging method might decrease spatial variation in PM 2.5 exposures (Lee et al 2014;Mulholland et al 1998). Spatiotemporal LUR models are supposed to generate results with fine resolution and high variations for both short-and long-term outdoor exposures (Dons et al 2014), which are aimed at promoting epidemiological and risk assessment studies in Beijing.…”

Section: Outdoor Exposuresupporting

confidence: 75%

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Peng

et al. 2014

Environ Sci Pollut Res

133

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Fine particulate matter (PM 2.5 ) is the major air pollutant in Beijing, posing serious threats to human health. Land use regression (LUR) has been widely used in predicting spatiotemporal variation of ambient air-pollutant concentrations, though restricted to the European and North American context. We aimed to estimate spatiotemporal variations of PM 2.5 by building separate LUR models in Beijing. Hourly routine PM 2.5 measurements were collected at 35 sites from 4th March 2013 to 5th March 2014. Seventy-seven predictor variables were generated in GIS, including street network, land cover, population density, catering services distribution, bus stop density, intersection density, and others. Eight LUR models were developed on annual, seasonal, peak/non-peak, and incremental concentration subsets. The annual mean concentration across all sites is 90.7 μg/m 3 (SD=13.7). PM 2.5 shows more temporal variation than spatial variation, indicating the necessity of building different models to capture spatiotemporal trends. The adjusted R 2 of these models range between 0.43 and 0.65. Most LUR models are driven by significant predictors including major road length, vegetation, and water land use. Annual outdoor exposure in Beijing is as high as 96.5 μg/m 3 . This is among the first LUR studies implemented in a seriously air-polluted Chinese context, which generally produce acceptable results and reliable spatial air-pollution maps. Apart from the models for winter and incremental concentration, LUR models are driven by similar variables, suggesting that the spatial variations of PM 2.5 remain steady for most of the time. Temporal variations are explained by the intercepts, and spatial variations in the measurements determine the strength of variable coefficients in our models.

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Section: Outdoor Exposuresupporting

confidence: 75%

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Peng

et al. 2014

Environ Sci Pollut Res

133

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“…This finding confirms that valuable LUR models can be developed in the absence of traffic count data, which are unreliable or nonexistent in many areas [37]. Many other scholars have also used road patterns to build LUR models [47,48]. Since it is difficult to collect the traffic flow data in Beijing, road classifications were selected in this study.…”

Section: Road Traffic Datasupporting

confidence: 59%

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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“…Although researchers chose different predictor variables according to local environments in their LUR models, most of them used linear regression to model the relationship between the pollutant level and the predictor variables [13,15,16,[77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95]. One of the assumptions for linear regression is that the observations should be independent of each other.…”

Section: Land-use Regression Modelsmentioning

confidence: 99%

A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

Xie

Šemanjski

Gautama

et al. 2017

IJGI

147

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Abstract:The impact of urban air pollution on the environments and human health has drawn increasing concerns from researchers, policymakers and citizens. To reduce the negative health impact, it is of great importance to measure the air pollution at high spatial resolution in a timely manner. Traditionally, air pollution is measured using dedicated instruments at fixed monitoring stations, which are placed sparsely in urban areas. With the development of low-cost micro-scale sensing technology in the last decade, portable sensing devices installed on mobile campaigns have been increasingly used for air pollution monitoring, especially for traffic-related pollution monitoring. In the past, some reviews have been done about air pollution exposure models using monitoring data obtained from fixed stations, but no review about mobile sensing for air pollution has been undertaken. This article is a comprehensive review of the recent development in air pollution monitoring, including both the pollution data acquisition and the pollution assessment methods. Unlike the existing reviews on air pollution assessment, this paper not only introduces the models that researchers applied on the data collected from stationary stations, but also presents the efforts of applying these models on the mobile sensing data and discusses the future research of fusing the stationary and mobile sensing data.

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Land use regression models for estimating individual NOx and NO2 exposures in a metropolis with a high density of traffic roads and population

Cited by 108 publications

References 21 publications

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

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

A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

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