ArticleEnvironmental science and pollution research international2015
Applying land use regression model to estimate spatial variation of PM₂.₅ in Beijing, China.
Article in Environmental science and pollution research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed.
- Air quality prediction and control systems using machine learning and adaptive neuro-fuzzy inference system.Heliyon · 2024Article
- Spatiotemporal characteristics and socioeconomic factors of PMChemosphere · 2023Article
- High-resolution patterns and inequalities in ambient fine particle mass (PMThe Science of the total environment · 2023Article
- Article
- Prediction of PM2.5 time series by seasonal trend decomposition-based dendritic neuron model.Neural computing & applications · 2023Article
- The Spatial Interaction Effect of Green Spaces on Urban Economic Growth: Empirical Evidence from China.International journal of environmental research and public health · 2022Article
- Acute effect of air pollutants' peak-hour concentrations on ischemic stroke hospital admissions among hypertension patients in Beijing, China, from 2014 to 2018.Environmental science and pollution research international · 2022Article
- Spatio-Temporal Variation-Induced Group Disparity of Intra-Urban NOInternational journal of environmental research and public health · 2022Article
- Spatialization and Prediction of Seasonal NOInternational journal of environmental research and public health · 2022Article
- Using Multisource Data to Assess PMInternational journal of environmental research and public health · 2022Article
- Self-adaptive bandwidth eigenvector spatial filtering model for estimating PMEnvironmental science and pollution research international · 2021Article
- The impacts of the built environment on the incidence rate of COVID-19: A case study of King County, Washington.Sustainable cities and society · 2021Article
- Assessment of the Dynamic Exposure to PMInternational journal of environmental research and public health · 2021Article
- A new buffer selection strategy for land use regression model of PMEnvironmental science and pollution research international · 2021Article
- Spatial differentiation of the NPP and NDVI and its influencing factors vary with grassland type on the Qinghai-Tibet Plateau.Environmental monitoring and assessment · 2021Article
- Spatial distribution characteristics of PMSustainable cities and society · 2020Article
- Application of land use regression to assess exposure and identify potential sources in PMAtmospheric environment (Oxford, England : 1994) · 2020Article
- Assessment of the spatio-temporal pattern of PMEnvironmental monitoring and assessment · 2020Article
- Effect of Urban Greening on Incremental PMFrontiers in public health · 2020Article
- Land use regression for spatial distribution of urban particulate matter (PMEnvironmental monitoring and assessment · 2019Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Fine particulate matter (PM2.5) 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 PM2.5 by building separate LUR models in Beijing. Hourly routine PM2.5 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). PM2.5 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 PM2.5 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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