Light pollution assessment model based on spatial heterogeneity analysis and a linear coregionalization model: a study case in Tianjin Municipality (North China)

• A novel model integrating spatial statistics and LCM assesses light pollution. • Grid monitoring with Moran’s I and SCLPI quantifies heterogeneity in high-activity areas. • LCM clustering classifies Tianjin’s districts, identifying key high-risk zones. • Validated LPI precisely pinpoints areas requiring management intervention. With the acceleration of urbanization and economic development, the spread and intensity of artificial lighting have increased dramatically on a global scale, which has resulted in ubiquitous light spillage. Light pollution has emerged as a significant threat to both the environment and human health. This not only hampers astronomical observations but also endangers biodiversity, disrupts the distribution and habitats of flora and fauna, and exacerbates climate change. In this study, Tianjin was divided into gridded monitoring areas. A light pollution evaluation model was then established based on both spatial statistical analysis and linear coregionalization methods (LCM). This model was subsequently applied to analyze the degree of light pollution and the regional correlation of each monitoring point in Tianjin. We also developed a set of comprehensive management standard that incorporate factors influencing urban light pollution. By applying this model, the current state of light pollution in Tianjin was directly assessed, its spatial heterogeneity was uncovered, and high-risk areas within the city were precisely identified. Our findings not only reveal the extent of light pollution in Tianjin but also offer valuable insights for other cities seeking to enhance and optimize their night-time lighting environments. This study addresses the important environmental issue of light pollution and contributes to the protection of ecosystems and human health by offering a scientific basis for light pollution management and mitigation.