ArticleiScience2023
The rapid increase of urban contaminated sites along China's urbanization during the last 30 years.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Legacy contamination in Chicago urban park soils: Spatial patterns, socioeconomic disparities, and implications for urban agriculture.iScience · 2026Article
- Machine learning coupled with causal inference to identify COVID-19 related chemicals that pose a high concern to drinking water.iScience · 2024Article
- Review
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Authors and funding
3 authors.
Funding
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Abstract
Contaminated sites pose serious threats to the soil environment and human health. However, the location and temporal changes of urban contaminated sites across China remain unknown due to data scarcity. Here, we developed a machine-learning model to identify the contaminated sites using public data. Results show that the trained model with 2,005 surveyed site samples and six variables can achieve a model performance evaluation value of 0.86. 43,676 contaminated sites were identified from 83,498 polluting enterprise plots in China. However, these contaminated sites have significant spatiotemporal heterogeneity, mainly located in economically developed provinces, urban agglomerations, and core urban areas. Moreover, the contaminated sites increased by 325% along with urban expansion from 1990 to 2018. The abandoned contaminated sites increased rapidly, but the contaminated sites in production decreased continuously. This methodological framework and our findings contribute to the precise management of contaminated sites and provide insights into urban sustainable development.
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Registered trials
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