Spatial distribution characteristics of PM2.5 and PM10 in Xi’an City predicted by land use regression models

Han, Li; Zhao, Jingyuan; Gao, Yuejing; Gu, Zhengqi; Xin, Kai; Zhang, Jianxin

doi:10.1016/j.scs.2020.102329

Cited by 72 publications

(27 citation statements)

References 46 publications

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“…Xi’an, the capital of Shaanxi Province and an important central city in western China, is located in the Guanzhong Basin in the middle of the Yellow River watershed in eastern Northwest China, between 107°40’-109°49’ east longitude and 33°42’-34°45’ north latitude; it is adjacent to the Weihe River and Loess Plateau in the north and the Qinling Mountains in the south [ 26 ]. The jurisdiction is approximately 204 km long from east to the west and 116 km wide from north to south.…”

Section: Methodsmentioning

confidence: 99%

Analysis of the spatial differentiation and scale effects of the three-dimensional architectural landscape in Xi’an, China

Ren

Zhao

et al. 2021

PLoS ONE

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Three-dimensional landscape patterns are an effective means to study the relationship between landscape pattern evolution and eco-environmental effects. This paper selects six districts in Xi’an as the study area to examine the spatial distribution characteristics of the three-dimensional architectural landscape in the city’s main urban area using three-dimensional information on the buildings in 2020 with the support of GIS. In this study, two new architectural landscape indices—landscape height variable coefficient and building rugosity index—were employed in landscape pattern analysis, whilst a system of rigorous and comprehensive three-dimensional architectural landscape metrics was established using principal component analysis. A mathematical model of weighted change of landscape metrics based on the objective weighting method was applied to carry out scale analysis of the landscape patterns. Spatial statistical analysis and spatial autocorrelation analysis were conducted to comprehensively study the differentiation of three-dimensional architectural landscape spatial patterns. The results show that the characteristic scale of the three-dimensional landscape pattern in Xi’an’s main urban area is around 8 km. Moreover, the three-dimensional landscape of the buildings in this area is spatially positively correlated, exhibiting a high degree of spatial autocorrelation whilst only showing small spatial differences. The layout of the architectural landscape pattern is disorderly and chaotic within the second ring, whilst the clustering of patch types occurs near the third ring. Moreover, the building density in the Beilin, Lianhu, and Xincheng districts is large, the building height types are rich, and the roughness of the underlying surface is high, such that these are key areas to be improved through urban renewal. The height, volume, density, morphological heterogeneity, and vertical roughness of the architectural landscape vary amongst functional areas within the study area. This paper is the first to apply the study of spatial heterogeneity of three-dimensional landscape patterns to Xi’an. It does so in order to provide a quantitative basis for urban landscape ecological design for urban renewal and the rational planning of built-up areas, which will promote the sustainable development of the city’s urban environment.

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Section: Methodsmentioning

confidence: 99%

Analysis of the spatial differentiation and scale effects of the three-dimensional architectural landscape in Xi’an, China

Ren

Zhao

et al. 2021

PLoS ONE

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“…Several machine learning methods have been used in air pollution monitoring and prediction in the past few years, including deep neural networks (Karimian et al, 2019), extreme gradient boosting models (Xu et al, 2018), kriging (Li et al, 2019) and random forests (Brokamp et al, 2017). Some studies use non-spatial regression models for air pollution analyses, such as ordinary least squares (OLS) models (Zhao et al, 2019), quantile regression models (Xu & Lin, 2020) or land-use regression models (Han et al, 2020). However, since air pollution is an inherently spatial issue, many studies employ spatial regression models, such as a spatial lag model (SLM) (Ren & Matsumoto, 2020), a spatial error model (Zhou et al, 2018) or a spatial Durbin model (Chen et al, 2017).…”

Section: State Of the Artmentioning

confidence: 99%

Greenwashing in the US Metal Industry? A Novel Approach Combining SO2 Concentrations From Satellite Data, a Plant-Level Firm Database and Web Text Mining

et al. 2022

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“…According to the state of the Global Climate 2020 from the World Meteorological Organization (WMO) (https://public.wmo.int/en, accessed on 27 October 2021), the global mean temperature of 2020 was 1.2 • C beyond the baseline from 1850 to 1900, indicating that the issue of global warming is getting worse. In addition, serious problems such as the emission of greenhouse gases [10], atmospheric pollution [11,12], and building energy consumption [13] will become more serious because of global warming. Moreover, higher temperatures accelerate blood circulation and increase skin temperature in the human body, which can easily cause heat stroke and even death [14].…”

Section: Introductionmentioning

confidence: 99%

Architectural Simulations on Spatio-Temporal Changes of Settlement Outdoor Thermal Environment in Guanzhong Area, China

et al. 2022

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This paper aims to provide data support for rural sustainable development through analyzing the spatio-temporal characteristics of the interactions of the outdoor thermal environment. The ordinary and representative rural settlements in the Guanzhong area were selected to analyze the dynamic process of the rural thermal environment through field measurements and numerical simulations. RMSE (root mean square error) and MAPE (mean absolute percentage) were used to verify the numerical simulation model, and physiological equivalent temperature (PET) was used to evaluate the outdoor thermal environment. Results show that the ENVI-met model reliably predicts the thermal environment of a rural settlement, as the air temperature and relative humidity values range of the RMSE and MAPE were 0.85–1.79 and 2.04–5.11%, respectively. Moreover, the air temperature rose by 3.08% and relative humidity dropped by 4.42% from 2003 to 2018 as the amount of artificial surfaces increased by 35.4% and the PET index gradually increased by 27.43% at daytime and 34.03% at nighttime. Furthermore, trees could improve the outdoor thermal environment significantly, mainly because the average air temperature decreased by 3.6% and relative humidity increased by 8%, and the PET index decreased by 12.4% and 13.1%, respectively, for daytime and nighttime. This case study is representative of rural settlements in the Guanzhong plain, and thus is an appeal to rural planners to pay attention to the thermal environment issues caused by increased artificial underlay surfaces and to focus on trees in rural areas.

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Spatial distribution characteristics of PM2.5 and PM10 in Xi’an City predicted by land use regression models

Cited by 72 publications

References 46 publications

Analysis of the spatial differentiation and scale effects of the three-dimensional architectural landscape in Xi’an, China

Analysis of the spatial differentiation and scale effects of the three-dimensional architectural landscape in Xi’an, China

Greenwashing in the US Metal Industry? A Novel Approach Combining SO2 Concentrations From Satellite Data, a Plant-Level Firm Database and Web Text Mining

Architectural Simulations on Spatio-Temporal Changes of Settlement Outdoor Thermal Environment in Guanzhong Area, China

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