Land use regression models to estimate the annual and seasonal spatial variability of sulfur dioxide and particulate matter in Tehran, Iran

Amini, Heresh; Shahri, Seyed Mahmood Taghavi; Henderson, Sarah B.; Naddafi, Kazem; Nabizadeh, Ramin; Yunesian, Masud

doi:10.1016/j.scitotenv.2014.04.106

Cited by 108 publications

(87 citation statements)

References 51 publications

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“…Quality assurance/quality control (QA/QC) procedures at each site were followed according to the environmental protection standards of China (HJ 618-2011;MEPCN 2011). Data loss was inevitable during the measurement, and different approaches were adopted to deal with the missing data in existing LUR models (Amini et al 2014;Gulliver et al 2011;Ross et al 2013). Since there were few missing data in our measurements (Supplementary Table SI1), we did not discard any sites or any periods, and the original dataset was used for analysis.…”

Section: Pm 25 Measurementsmentioning

confidence: 99%

“…In order to limit the estimated concentrations to a reasonable range, predicted concentrations at the low and high ends were truncated at a certain percentage of measured maximum/minimum values in some LUR studies (Abernethy et al 2013;Amini et al 2014;Henderson et al 2007). There is no rigorous standard for cutting the range of predicted concentrations.…”

Section: Regression Mapping and Population Exposurementioning

confidence: 99%

“…There is no rigorous standard for cutting the range of predicted concentrations. We made similar assumptions described by Amini et al (2014) that predicted concentrations were truncated at 120 % of the maximum and 60 % of the minimum measured values.…”

Section: Regression Mapping and Population Exposurementioning

confidence: 99%

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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: Pm 25 Measurementsmentioning

confidence: 99%

Section: Regression Mapping and Population Exposurementioning

confidence: 99%

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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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“…It has demonstrated that the mass concentrations of total suspended particulate (TSP), PM10 and PM 2.5 vary in different areas within a same city (10)(11)(12)(13). Researches particularly prove the significant influences of land use types (14)(15)(16)(17)(18) and building density (19)(20)(21), green space (10), water body (22), location of highway entrance (23), traffic patterns (24,25) and street scale (26,27) upon particulate matter concentration and distribution.…”

Section: Introductionmentioning

confidence: 99%

Correlation analysis of lung cancer and urban spatial factor: based on survey in Shanghai

Wang

Zhao

et al. 2016

J. Thorac. Dis.

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Background: The density of particulate matter (PM) in mega-cities in China such as Beijing and Shanghai has exceeded basic standards for health in recent years. Human exposure to PMs has been identified as traceable and controllable factor among all complicated risk factors for lung cancer. While the improvement of air quality needs tremendous efforts and time, certain revision of PM's density might happen associated with the adjustment of built environment. It is also proved that urban built environment is directly relevant to respiratory disease. Studies have respectively explored the indoor and outdoor factors on respiratory diseases. More comprehensive spatial factors need to be analyzed to understand the cumulative effect of built environment upon respiratory system. This interdisciplinary study examines the impact of both indoor (including age of housing, interval after decoration, indoor humidity etc.) and outdoor spatial factors (including density, parking, green spaces etc.) on lung cancer. Methods: A survey of lung cancer patients and a control group has been conducted in 2014 and 2015. A total of 472 interviewees are randomly selected within a pool of local residents who have resided in Shanghai for more than 5 years. Data are collected including their socio-demographic factors, lifestyle factors, and external and internal residential area factors. Regression models are established based on collected data to analyze the associations between lung cancer and urban spatial factors. Results: Regression models illustrate that lung cancer presents significantly associated with a number of spatial factors. Significant outdoor spatial factors include external traffic volume (P=0.003), main plant type (P=0.035 for trees) of internal green space, internal water body (P=0.027) and land use of surrounding blocks (P=0.005 for residential areas of 7-9 floors, P=0.000 for residential areas of 4-6 floors, P=0.006 for business/ commercial areas over 10 floors, P=0.005 for business/commercial areas of 7-9 floors, P=0.043 for business/ commercial areas of 4-6 floors). Indoor spatial factors include age of housing (P=0.003) and indoor humidity (P=0.000). Conclusions: The findings support the hypothesis that both indoor and outdoor spatial factors are independently associated with lung cancer incidence. Certain principles based on the modeling results are proposed to revise the criteria for lung cancer screening of high-risk individuals. It also provides empirical evidence for urban planning and design to improve built environment with more thorough consideration of respiratory health.

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“…The LUR model combines the monitoring of air pollution within a certain spatial scale and the development of stochastic models using GIScollected predictor variables, while interpolation methods (e.g., kriging and inverse distance weighting) are used to provide predictions of the spatial distribution of pollutants [1]. The most common predictor variables in previous studies that are of interest to various traffi c descriptions (e.g., intensity, congestion, road length, and distance) are population density, housing, land use, physical geography (e.g., elevation and distance to the sea), and meteorological data [2][3][4]. Further developments of LUR modeling have included greater focus on transferable models and predictor variables such as wind speed, wind direction, and emission data; further development of models were considered [5,6].…”

Section: Introductionmentioning

confidence: 99%

Land Use Regression Models Using Satellite Aerosol Optical Depth Observations and 3D Building Data from the Central Cities of Liaoning Province, China

Gong¹,

Hu²,

Liu³

et al. 2016

Pol. J. Environ. Stud.

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Land use regression (LUR) modeling is a promising method for assessing the spatial variation of air pollutant concentrations. We developed an LUR model for air pollutants (SO 2 , NO 2 , and PM 10 ) in the central cities of Liaoning Province using monitoring data collected during 2013. We evaluated whether the addition of annual satellite aerosol optical depth (AOD) observations and fi ve canyon indicators (building height, building coverage ratio, fl oor area ratio, building shape coeffi cient, and high-rise building ratio) improved the LUR models. Out-of-sample "10-fold" cross validation was used to quantify the accuracy of the model predictions. Our results showed that the gross domestic product (GDP) and the distance to the nearest industrial emissions were the common variables for the models. Annual AOD demonstrated weak correlations with air pollutant concentrations because of its instantaneity, low resolution, and limited precision; however, it was useful for improving the coeffi cient of determination (R 2 ) of the LUR models. The full models incorporating the annual AOD data and canyon indicators showed further improvement. The improvements of R 2 were 0.22, 0.19, and 0.39 for SO 2 , NO 2 , and PM 10 , respectively, demonstrating that the consideration of canyon indicators could still be valuable and could be used in LUR models.

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Land use regression models to estimate the annual and seasonal spatial variability of sulfur dioxide and particulate matter in Tehran, Iran

Cited by 108 publications

References 51 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

Correlation analysis of lung cancer and urban spatial factor: based on survey in Shanghai

Land Use Regression Models Using Satellite Aerosol Optical Depth Observations and 3D Building Data from the Central Cities of Liaoning Province, China

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