Spatial autocorrelation may bias the risk estimation: An application of eigenvector spatial filtering on the risk of air pollutant on asthma

Park, Yujin; Kim, Su Hwan; Kim, Seong Pyo; Ryu, Jiwon; Yi, Jinyeong; Kim, Jin Youp; Yoon, Hyung‐Jin

doi:10.1016/j.scitotenv.2022.157053

Cited by 12 publications

(4 citation statements)

References 42 publications

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“…It has been shown that spatial autocorrelation can analyse the correlation of the same variable in different spatial locations and is a measure of the degree of agglomeration in a spatial domain. At present, it is mostly applied to studies where the economic geography has obvious polarization and diffusion effects or the ecological environment has obvious regional differentiation characteristics [50,51]. In this paper, global spatial autocorrelation and local spatial autocorrelation are introduced to identify the spatial agglomeration characteristics of carbon sink performance of aggregated green infrastructure in metropolitan areas.…”

Section: Carbon Sink Performance Of Urban Aggregated Green Infrastruc...mentioning

confidence: 99%

Carbon Sink Performance Evaluation and Socioeconomic Effect of Urban Aggregated Green Infrastructure Based on Sentinel-2A Satellite

Cheng

Huang

Chen

et al. 2022

Forests

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Aggregated green infrastructure is the only element that has a relatively concentrated and well-functioning carbon sink in the city. It plays an important role in achieving carbon neutrality in urban areas with dense functions and scarce carbon sink resources. However, in contrast to other regions, aggregated green infrastructure carbon sink performance is more influenced by socioeconomic activities in urban centres. There is a lack of research on the impact between carbon sink performance and socioeconomic activities at the urban scale. In this study, we evaluated the carbon sink performance (i.e., carbon sink and location entropy) of aggregated green infrastructure and its interaction with socioeconomic activities at the urban scale based on Sentinel-2A satellite. The results showed that: (1) Aggregate green infrastructures with high carbon sink performance have significant aggregation characteristics in urban areas. (2) Aggregated green infrastructure with poor carbon sink performance tended to be surrounded by dense socioeconomic activities. Our study provides a new approach to the assessment of carbon sink performance of aggregated green infrastructure at the urban scale. More importantly, we make a new attempt to assess the association between carbon sink performance and socioeconomic activities of urban aggregated green infrastructure. These results point to a new direction for the realization of carbon neutrality in cities.

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Section: Carbon Sink Performance Of Urban Aggregated Green Infrastruc...mentioning

confidence: 99%

Carbon Sink Performance Evaluation and Socioeconomic Effect of Urban Aggregated Green Infrastructure Based on Sentinel-2A Satellite

Cheng

Huang

Chen

et al. 2022

Forests

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“…When investigating spatial associations, the base-ground methodology usually applied is spatial autocorrelation [3,5]; however, this analysis is known to affect the estimation of pollution effects [8], a particularly relevant aspect when addressing their impact on human health [9,10] by the exposure-response relationship and, in general, when developing environmental policies [4]. Specifically, the literature indicates that including residual spatial error terms improves the prediction of adverse health effects [9], as well as removing bias due to spatial patterns does, and is thus beneficial to the robustness of spatial correlation models [8], especially when estimating the covariate effect [11], therefore representing a critical adjustment to be made in spatial modelling. From a methodological viewpoint, the base on which spatial autocorrelation should be studied and modeled is the Local Indication of Spatial Association (LISA) approach [12].…”

Section: Introductionmentioning

confidence: 99%

Exploring Spatial–Temporal Patterns of Air Pollution Concentration and Their Relationship with Land Use

Gianquintieri,

Mahakalkar,

Caiani

2024

Atmosphere

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Understanding the spatial–temporal patterns of air pollution is crucial for mitigation strategies, a task fostered nowadays by the generation of continuous concentration maps by remote sensing technologies. We applied spatial modelling to analyze such spatial–temporal patterns in Lombardy, Italy, one of the most polluted regions in Europe. We conducted monthly spatial autocorrelation (global and local) of the daily average concentrations of PM2.5, PM10, O3, NO2, SO2, and CO from 2016 to 2020, using 10 × 10 km satellite data from the Copernicus Atmosphere Monitoring Service (CAMS), aggregated on districts of approximately 100,000 population. Land-use classes were computed on identified clusters, and the significance of the differences was evaluated through the Wilcoxon rank-sum test with Bonferroni correction. The global Moran’s I autocorrelation was overall high (>0.6), indicating a strong clustering. The local autocorrelation revealed high–high clusters of PM2.5 and PM10 in the central urbanized zones in winter (January–December), and in the agrarian southern districts in summer and autumn (May–October). The temporal decomposition showed that values of PMs are particularly high in winter. Low–low clusters emerged in the northern districts for all the pollutants except O3. Seasonal peaks for O3 occurred in the summer months, with high–high clusters mostly in the hilly and mildly urban districts in the northwest. These findings elaborate the spatial patterns of air pollution concentration, providing insights for effective land-use-based pollution management strategies.

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“…There is also demonstrated utility of considering spatial dependence in models investigating air pollution exposure through an environmental justice lens [16,[29][30][31]. Park et al [32] suggests that the lack of consideration for spatial autocorrelation may have biased previous assessments of air pollution's influence on asthma risk and shows that dealing with spatial autocorrelation can create stronger asthma risk models.…”

Section: Introductionmentioning

confidence: 99%

Spatial Analysis of Intra-Urban Air Pollution Disparities through an Environmental Justice Lens: A Case Study of Philadelphia, PA

Scolio,

Borha,

Kremer

et al. 2024

Atmosphere

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Urban air pollution has been long understood as a critical threat to human health worldwide. Worsening urban air quality can cause increased rates of asthma, respiratory illnesses, and mortality. Air pollution is also an important environmental justice issue as it disproportionately burdens populations made vulnerable by their socioeconomic and health status. Using spatially continuous fine-scale air quality data for the city of Philadelphia, this study analyzed the relationship between two air pollutants: particulate matter (PM2.5, black carbon (BC), and three dimensions of vulnerability: social (non-White population), economic (poverty), and health outcomes (asthma prevalence). Spatial autoregressive models outperformed Ordinary Least Squares (OLS) regression, indicating the importance of considering spatial autocorrelation in air pollution-related environmental-justice modeling efforts. Positive relationships were observed between PM2.5 concentrations and the socioeconomic variables and asthma prevalence. Percent non-White population was a significant predictor of BC for all models, while percent poverty was shown to not be a significant predictor of BC in the best fitting model. Our findings underscore the presence of distributive environmental injustices, where marginalized communities may bear a disproportionate burden of air pollution within Philadelphia.

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Spatial autocorrelation may bias the risk estimation: An application of eigenvector spatial filtering on the risk of air pollutant on asthma

Cited by 12 publications

References 42 publications

Carbon Sink Performance Evaluation and Socioeconomic Effect of Urban Aggregated Green Infrastructure Based on Sentinel-2A Satellite

Carbon Sink Performance Evaluation and Socioeconomic Effect of Urban Aggregated Green Infrastructure Based on Sentinel-2A Satellite

Exploring Spatial–Temporal Patterns of Air Pollution Concentration and Their Relationship with Land Use

Spatial Analysis of Intra-Urban Air Pollution Disparities through an Environmental Justice Lens: A Case Study of Philadelphia, PA

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