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Study on the relationship between PM2.5 concentration and intensive land use in Hebei Province based on a spatial regression model.

ABSTRACT: Based on 0.01°×0.01° grid data of PM2.5 annual concentration and statistical yearbook data for 11 cities in Hebei Province from 2000 to 2015, the temporal and spatial distribution characteristics of PM2.5 in the study area are analysed, the level of intensive land use in the area is evaluated, and decoupling theory and spatial regression are used to discuss the relationship between PM2.5 concentration and intensive land use and the influence of intensive land use variables on PM2.5 in Hebei Province. The results show that 1. In terms of time, the concentration of PM2.5 in Hebei Province showed an overall upward trend from 2000 to 2015, with the highest in winter and the lowest in summer. The daily variations show double peaks at 8:00-10:00 and 21:00-0:00 and a single valley at 16:00-18:00. 2. In terms of space, the concentration of PM2.5 in Hebei Province is high in the southeast and low in the northwest, and the pollution spillover initially decreases and then increases. 3. In the past 16 years, the level of intensive land use in Hebei Province has increased annually, but blind expansion still exists. 4. Decoupling theory and the spatial lag model show that land use intensity, land input level and land use structure are positively correlated with PM2.5 concentration, land output benefit is negatively correlated with PM2.5 concentration, and PM2.5 concentration and land intensive use level have not yet been decoupled; thus, the relationship is not harmonious. This research can provide a scientific basis for reducing air pollution and promoting the development of urban land resources for intensive and sustainable development.

SUBMITTER: Shao J

PROVIDER: S-EPMC7500636 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Study on the relationship between PM2.5 concentration and intensive land use in Hebei Province based on a spatial regression model.

Shao Jingjing J Ge Jingfeng J Feng Xiaomiao X Zhao Chaoran C

PloS one 20200918 9

Based on 0.01°×0.01° grid data of PM2.5 annual concentration and statistical yearbook data for 11 cities in Hebei Province from 2000 to 2015, the temporal and spatial distribution characteristics of PM2.5 in the study area are analysed, the level of intensive land use in the area is evaluated, and decoupling theory and spatial regression are used to discuss the relationship between PM2.5 concentration and intensive land use and the influence of intensive land use variables on PM2.5 in Hebei Prov ...[more]

PMID: 32946497

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Project description:BackgroundOxidative potential (OP) has been suggested to be a more health-relevant metric than particulate matter (PM) mass. Land use regression (LUR) models can estimate long-term exposure to air pollution in epidemiological studies, but few have been developed for OP.ObjectivesWe aimed to characterize the spatial contrasts of two OP methods and to develop and evaluate LUR models to assess long-term exposure to the OP of PM2.5.MethodsThree 2-week PM2.5 samples were collected at 10 regional background, 12 urban background, and 18 street sites spread over the Netherlands/Belgium in 1 year and analyzed for OP using electron spin resonance (OP(ESR)) and dithiothreitol (OP(DTT)). LUR models were developed using temporally adjusted annual averages and a range of land-use and traffic-related GIS variables.ResultsStreet/urban background site ratio was 1.2 for OP(DTT) and 1.4 for OP(ESR), whereas regional/urban background ratio was 0.8 for both. OP(ESR) correlated moderately with OP(DTT) (R2 = 0.35). The LUR models included estimated regional background OP, local traffic, and large-scale urbanity with explained variance (R2) of 0.60 for OP(DTT) and 0.67 for OP(ESR). OP(DTT) and OP(ESR) model predictions were moderately correlated (R2 = 0.44). OP model predictions were moderately to highly correlated with predictions from a previously published PM2.5 model (R2 = 0.37-0.52), and highly correlated with predictions from previously published models of traffic components (R2 > 0.50).ConclusionLUR models explained a large fraction of the spatial variation of the two OP metrics. The moderate correlations among the predictions of OP(DTT), OP(ESR), and PM2.5 models offer the potential to investigate which metric is the strongest predictor of health effects.CitationYang A, Wang M, Eeftens M, Beelen R, Dons E, Leseman DL, Brunekreef B, Cassee FR, Janssen NA, Hoek G. 2015. Spatial variation and land use regression modeling of the oxidative potential of fine particles. Environ Health Perspect 123:1187-1192; http://dx.doi.org/10.1289/ehp.1408916.

Dataset Information

Study on the relationship between PM2.5 concentration and intensive land use in Hebei Province based on a spatial regression model.

Publications

Study on the relationship between PM2.5 concentration and intensive land use in Hebei Province based on a spatial regression model.

Similar Datasets

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