Effect of Land Use and Cover Change on Air Quality in Urban Sprawl

Zou, Bin; Xu, Shan; Sternberg, Troy; Fang, Xin

doi:10.3390/su8070677

Cited by 59 publications

(28 citation statements)

References 35 publications

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“…According to the previous LUR research findings on the selection of geographical feature characteristics [14,[29][30][31], data collected for LUR modelling in this study contains annual average PM2.5 concentrations, elevation, AOD, climate characteristics (temperature, wind speed, relative humidity, atmospheric pressure, and precipitation), road traffic, land use and cover, industrial plants, and surface dust. The distribution of PM2.5 monitoring sites and the partial basic geographical feature data, within the BTH region during the study period of 1 January 2015 to 31 December 2015, are shown in Figure 1.…”

Section: Study Area and Data Collectionmentioning

confidence: 99%

A Generalized Additive Model Combining Principal Component Analysis for PM2.5 Concentration Estimation

Zhai

Zou

et al. 2017

IJGI

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Abstract:As an extension of the traditional Land Use Regression (LUR) modelling, the generalized additive model (GAM) was developed in recent years to explore the non-linear relationships between PM 2.5 concentrations and the factors impacting it. However, these studies did not consider the loss of information regarding predictor variables. To address this challenge, a generalized additive model combining principal component analysis (PCA-GAM) was proposed to estimate PM 2.5 concentrations in this study. The reliability of PCA-GAM for estimating PM 2.5 concentrations was tested in the Beijing-Tianjin-Hebei (BTH) region over a one-year period as a case study. The results showed that PCA-GAM outperforms traditional LUR modelling with relatively higher adjusted R 2 (0.94) and lower RMSE (4.08 µg/m 3 ). The CV-adjusted R 2 (0.92) is high and close to the model-adjusted R 2 , proving the robustness of the PCA-GAM model. The PCA-GAM model enhances PM 2.5 estimate accuracy by improving the usage of the effective predictor variables. Therefore, it can be concluded that PCA-GAM is a promising method for air pollution mapping and could be useful for decision makers taking a series of measures to combat air pollution.

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Section: Study Area and Data Collectionmentioning

confidence: 99%

A Generalized Additive Model Combining Principal Component Analysis for PM2.5 Concentration Estimation

Zhai

Zou

et al. 2017

IJGI

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“…Therefore, it is important to ensure the quality and evaluate the performance of the C6.1 AOD products for China. Furthermore, the properties of aerosol are known to be correlated to surface types (Wei et al, 2017;Zou et al, 2016) and geographical conditions (He et al, 2016;Li et al, 2003;Wang et al, 2010) in China. In the past, many scholars have undertaken excellent research to evaluate the AOD products for China (He et al, 2010(He et al, , 2017Tao et al, 2015;Wang et al, 2010;Wei et al, 2017;Xie et al, 2011), yet no one has simultaneously considered the specific land cover types and the digital elevation model (DEM).…”

Section: Introductionmentioning

confidence: 99%

Evaluation and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET over regions in China with multifarious underlying surfaces

Wang

Yuan

et al. 2019

Atmospheric Environment

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In this study, we evaluated the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) aerosol optical depth (AOD) products and compared them with the Collection 6 (C6) products over regions in China with multifarious underlying surfaces during 2001-2016. The AOD retrievals were validated against 20 AERONET (V3) sites, and the results show that the correlation coefficient (R) for dark target (DT) retrievals in C6.1 is 0.946, while the fraction within the expected error (EE) can be considered relatively low at only 54.03%. Deep blue (DB) retrievals in C6.1 have a slightly lower R value (0.931), but the other criteria are superior to DT. Comparing the results over urban and vegetation areas in C6.1, the overall quality of the DB retrievals is better than the DT retrievals in urban areas. The performance of DT is significantly superior to DB in the low elevation vegetation (LEV) areas. For the high elevation vegetation (HEV) areas, DB performs synthetically better than DT. In the spatial distribution aspect in C6.1, most of the DB AOD values are less than those of DT, and the relationship between DT and DB varies with the different land cover types. For the AOD coverage in C6.1, DT retrievals with high coverage mainly distribute in east-central China. However, the effects of high surface reflectance lead to low AOD coverage in the southwest. In contrast, the AOD coverage of DBtends to be high in areas where the main land cover type is bare soil and tends to be low in areas affected by snow. In terms of the comparison between C6.1 and C6, the overestimation of DT over urban areas in C6 is effectively mitigated in C6.1. However, a nearly systematic decline in DT is discovered in C6.1 as well. With respect to DB, consistent AOD coverage distribution is observed, with only subtle distinction. The AOD coverage of DB in C6.1 appears higher than that in C6 in the middle, south, and northeast of China. The quality of the DB retrievals in C6.1 increases slightly compared to C6, and the most remarkable improvement is observed for the coarse aerosol particles.

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“…To account for the regional variation of air quality, many researchers have examined the influencing factors relevant to urban air quality. In these studies, factors as meteorological elements, land use and land cover change, urbanization, industrialization, energy structure, and transport patterns have been reported to have significant impact on urban air quality (Krummel et al, 1984;Shaw et al, 2010;Tai et al, 2010;Zhang and Fan, 2011;Guan et al, 2014;Lu and Han, 2014;Patton et al, 2014;Li et al, 2015;Lin and Wang, 2016;Qin and Liao, 2016;Sun et al, 2016;Zou et al, 2016;Hu et al, 2017;Yan et al, 2017). The previous studies, in general, have predominantly observed the characteristics of determinants within a single region or among different cities.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal characteristics of urban air quality in China and geographic detection of their determinants

Zhu

Gong

2018

J. Geogr. Sci.

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Ambient air pollution brought by the rapid economic development and industrial production in China has exerted a significant influence on socio-economic activities and public health, especially in the densely populated urban areas. Therefore, scientific examination of regional variation of urban air quality and its dominant factors is of great importance to regional environmental management. Based on daily air quality index (AQI) datasets spanning from 2014 to 2016, this study analysed the spatiotemporal characteristics of air quality across different regions throughout China and ascertained the determinants of urban air quality in disparate regions. The main findings are as follows: (1) The annual average value of the urban AQI in China decreased from 2014 to 2016, indicating a desirable trend in air quality at the national scale.(2) The attainment rate of the urban AQI exhibited an apparent spatially stratified heterogeneity, wherein North China retained a high AQI value. The increase of Moran's I Index reported an apparent spillover effect among adjacent regions. (3) Both at the national and regional scales, the seasonal tendency of air quality in each year is similar, wherein good in summer and relatively poor in winter. (4) Results drawn from the Geographic Detector analysis show that dominant factors influencing AQI vary significantly across urban agglomerations. Topographical and meteorological variations in urban areas may lead to complex spatiotemporal variations in pollutant concentration. Whereas given the same natural conditions, the human-dominated factors, such as industrial structure and urban form, exert significant impacts on urban air quality.The spatial spillover effects and regional heterogeneity of urban air quality illustrated in this study suggest the governments and institutions should set priority to the importance of regional cooperation and collaboration in light of environment regulation and pollution prevention.

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Effect of Land Use and Cover Change on Air Quality in Urban Sprawl

Cited by 59 publications

References 35 publications

A Generalized Additive Model Combining Principal Component Analysis for PM2.5 Concentration Estimation

A Generalized Additive Model Combining Principal Component Analysis for PM2.5 Concentration Estimation

Evaluation and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET over regions in China with multifarious underlying surfaces

Spatiotemporal characteristics of urban air quality in China and geographic detection of their determinants

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