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Variation trend prediction of ground-level ozone concentrations with high-resolution using landscape pattern data.
Yingying Mei1, Xueqi Xiang2, Zhenwei Wang1
1School of Public Administration, Hubei University, Wuhan, China.
Plos One
|November 16, 2023
Summary
Ground-level ozone concentrations (GOCs) are rising in Shenzhen, particularly in western areas. Landscape heterogeneity increases GOCs, while dominance and contagion decrease them, guiding pollution control strategies.
Area of Science:
- Environmental Science
- Atmospheric Chemistry
- Spatial Analysis
Background:
- Ground-level ozone pollution poses a significant environmental and health risk.
- Understanding the relationship between landscape patterns and ground-level ozone concentrations (GOCs) is crucial for effective pollution control.
- Existing methods for predicting GOCs may lack spatial resolution or predictive accuracy.
Purpose of the Study:
- To develop and validate a hybrid model for predicting ground-level ozone concentration (GOC) variation trends.
- To analyze the spatiotemporal patterns of GOCs in Shenzhen and their correlation with landscape characteristics.
- To provide insights for landscape optimization to mitigate ground-level ozone pollution.
Main Methods:
- A hybrid model combining a generalized linear model (GLM) and a logistic regression model (LRM) was developed.
- The model was evaluated using percent of samples correctly predicted (PCP) and area under the receiver operating characteristics curve (AUC).
- Per-pixel probabilities of increasing annual average GOCs were generated at a 1 km spatial resolution for Shenzhen.
Main Results:
- Annual average GOCs in Shenzhen showed an increasing trend from 2015 to 2020, with higher concentrations in the west and lower in the east.
- GOC variation trends were significantly positively correlated with landscape heterogeneity (HET) and negatively correlated with dominance (DMG) and contagion (CON).
- High risk of increasing GOCs was identified in specific districts: GuangMing, PingShan, LongGang, LuoHu, and BaoAn.
Conclusions:
- The hybrid model demonstrates satisfactory performance in predicting GOC variation trends.
- Landscape heterogeneity is a key factor influencing GOC increases, while landscape configuration plays a mitigating role.
- The findings offer a preliminary index for GOC trend estimation without monitoring data and guide landscape design for ozone pollution control.
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