Chi-square tests for selected models.

Scientifically configuring landscape patterns based on their relationship with ground-level ozone concentrations (GOCs) is an effective way to prevent and control ground-level ozone pollution. In this paper, a GOC variation trend prediction model (hybrid model) combining a generalized linear model (GLM) and a logistic regression model (LRM) was established to analyze the spatiotemporal variation patterns in GOCs as well as their responses to landscape patterns. The model exhibited satisfactory performance, with percent of samples correctly predicted (PCP) value of 82.33% and area under receiver operating characteristics curve (AUC) value of 0.70. Using the hybrid model, the per-pixel rise probability of annual average GOCs at a spatial resolution of 1 km in Shenzhen were generated. The results showed that (1) annual average GOCs were increasing in Shenzhen from 2015 to 2020, and had obvious spatial differences, with a higher value in the west and a lower value in the east; (2) variation trend in GOCs was significant positively correlated with landscape heterogeneity (HET), while significant negatively correlated with dominance (DMG) and contagion (CON); (3) GOCs in Shenzhen has a great risk of rising, especially in GuangMing, PingShan, LongGang, LuoHu and BaoAn. The results provide not only a preliminary index for estimating the GOC variation trend in the absence of air quality monitoring data but also guidance for landscape optimizing design from the perspective of controlling ground-level ozone pollution.基于景观格局与近地面臭氧浓度（ground-level ozone concentrations, GOCs）的关联进行科学配置，是防控近地面臭氧污染的有效途径。本文构建了一种结合广义线性模型（generalized linear model, GLM）与逻辑回归模型（logistic regression model, LRM）的GOC变化趋势预测混合模型，用以分析GOC的时空变化格局及其对景观格局的响应。该模型表现出优异的预测性能，其正确预测样本百分比（percent of samples correctly predicted, PCP）达82.33%，受试者工作特征曲线下面积（area under receiver operating characteristics curve, AUC）为0.70。借助该混合模型，本研究生成了深圳市空间分辨率为1km的年平均GOC逐像元上升概率。研究结果显示：（1）2015至2020年深圳市年平均GOC呈上升趋势，且存在显著空间差异，西部浓度较高、东部较低；（2）GOC变化趋势与景观异质性（landscape heterogeneity, HET）呈显著正相关，与景观优势度（dominance, DMG）及景观蔓延度（contagion, CON）呈显著负相关；（3）深圳市GOC存在较高的上升风险，尤以光明、坪山、龙岗、罗湖及宝安区域为甚。本研究结果不仅可为缺乏空气质量监测数据场景下的GOC变化趋势估算提供初步参考指标，同时可为从近地面臭氧污染防控视角开展景观优化设计提供指导。