Rapid urban expansion is altering the distribution of environmental benefits and risks. As cities grow outward, newly developed areas may face elevated exposure to multiple environmental pressures, raising concerns about whether urbanization is inadvertently exposing population toward more adverse environmental conditions. To investigate this, we use population-weighted exposure model to quantify three key environmental exposures in China's newly built-up areas from 2000 to 2018: blue-green space exposure (BGE), PM 2.5 exposure (PME), and heat stress exposure (HSE). By integrating SOM–K-means clustering and Bayesian Networks, we identify the evolution of typical environmental exposure combinations and examine their conditional dependencies. Results show that, from 2010 to 2018, BGE slightly improves, PME shows greater balance but persistent risks, while HSE intensified significantly. Clustering analysis identifies three distinct environmental exposure types: Ecological Deficiency (ED), High Environmental Cost (HEC), and Eco-Advantaged under Heat (EAH). In 2010, ED is the most prevalent type (1133 counties; 55.5%), followed by HEC (792 counties; 38.8%) and EAH (115 counties; 5.6%). By 2018, EAH increases to 386 counties (18.4%), while ED and HEC decline to 1052 (50.1%) and 664 (31.6%) counties, respectively. Notably, 466 ED counties transition to HEC, making ED-to-HEC the largest single transition. 146 counties transition from ED to EAH, indicating ecological improvement accompanied by greater heat stress. Together, these changes reveal a pattern of urban expansion toward risks. Bayesian Networks further reveal a reconfiguration in the conditional dependency structure of environmental exposures, moving from a relatively constrained dependency pattern toward a more interconnected configuration, with BGE becoming more centrally embedded in the network. This study contributes a novel integrative framework for understanding how urban expansion reshapes population exposure to compound environmental pressures and provides insights to inform risk-aware and equitable urban governance.
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Pan et al. (2026) studied this question.
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