School of Public Administration, Hubei University, Wuhan, China.
Department of Sociology, Zhongnan University of Economics and Law, Wuhan, China.
PLoS One. 2023 Nov 16;18(11):e0294038. doi: 10.1371/journal.pone.0294038. eCollection 2023.
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.
基于与地面臭氧浓度（GOC）的关系科学配置景观格局是预防和控制地面臭氧污染的有效方法。本文建立了一种将广义线性模型（GLM）和逻辑回归模型（LRM）相结合的 GOC 变化趋势预测模型（混合模型），用于分析 GOC 的时空变化模式及其对景观格局的响应。该模型表现出令人满意的性能，样本百分比正确预测值（PCP）为 82.33%，接收器工作特征曲线下面积（AUC）值为 0.70。利用混合模型，生成了深圳市空间分辨率为 1km 的年均 GOC 上升概率的逐像素值。结果表明：（1）2015-2020 年深圳市年均 GOC 呈上升趋势，且空间差异明显，西部较高，东部较低；（2）GOC 的变化趋势与景观异质性（HET）显著正相关，与优势度（DMG）和聚集度（CON）显著负相关；（3）深圳市 GOC 上升风险较大，尤其是光明区、坪山区、龙岗区、罗湖区和宝安区。该结果不仅为缺乏空气质量监测数据时估计 GOC 变化趋势提供了初步指标，而且从控制地面臭氧污染的角度为景观优化设计提供了指导。
