Wang Runzi, Kim Jun-Hyun, Li Ming-Han
School for Environment and Sustainability, University of Michigan, 440 Church Street, Ann Arbor, MI 48109-1041, United States of America.
School of Planning, Design and Construction, Michigan State University, 552 W Circle Dr, East Lansing, MI 48823, United States of America.
Sci Total Environ. 2021 Mar 20;761:144057. doi: 10.1016/j.scitotenv.2020.144057. Epub 2020 Dec 14.
Urban development pattern significantly impacts stream water quality by influencing pollutant generation, build-up, and wash-off processes. It is thus necessary to understand and predict stream water quality in accordance with different urban development patterns to effectively advise urban growth planning and policies. To do so, we collected pollutant concentration data on nitrate (NO-N), total phosphate (TP), and Escherichia coli (E. coli) from 1047 sampling stations in the Texas Gulf Region. We utilized a Random Forest (RF) machine learning model to predict stream water quality under four planning scenarios with different urban densities and configurations. SHapley Additive exPlanations (SHAP) was used to prove the importance of urban development pattern in influencing stream water quality. The spatial variations of the impact of these patterns were explored with Geographically Weighted Regression (GWR). SHAP results indicated that Largest Patch Index (LPI), Patch Cohesion Index (COHESION), Splitting Index (SPLIT), and Landscape Division Index (DIVISION) were the most important urban development pattern metrics affecting stream water quality. The spatial variations of such patterns were shown to impact stream water quality depending on pollutants, seasonality, climate, and urbanization level. RF prediction results suggested that high density aggregated development was more effective in reducing TP and NO-N concentrations than the current sprawl development, but had the potential risk of increasing E. coli pollution in the wet season. The results of this study provide empirical evidence and a potential mechanistic explanation that stream water quality degradation is a consequence of urban sprawl. Lastly, machine learning is a powerful tool for scenario prediction in land use planning to forecast environmental impacts under different urban development pattern scenarios.
城市发展模式通过影响污染物的产生、积累和冲刷过程，对河流水质产生显著影响。因此，有必要根据不同的城市发展模式来理解和预测河流水质，以便有效地为城市发展规划和政策提供建议。为此，我们收集了德克萨斯湾地区1047个采样点的硝酸盐（NO-N）、总磷（TP）和大肠杆菌（E. coli）的污染物浓度数据。我们利用随机森林（RF）机器学习模型，预测了四种不同城市密度和布局规划情景下的河流水质。使用SHapley加性解释（SHAP）来证明城市发展模式对河流水质影响的重要性。利用地理加权回归（GWR）探索了这些模式影响的空间变化。SHAP结果表明，最大斑块指数（LPI）、斑块凝聚指数（COHESION）、分割指数（SPLIT）和景观分割指数（DIVISION）是影响河流水质的最重要的城市发展模式指标。这些模式的空间变化显示，根据污染物、季节性、气候和城市化水平，会对河流水质产生影响。RF预测结果表明，高密度聚集发展在降低TP和NO-N浓度方面比当前的蔓延式发展更有效，但在雨季有增加大肠杆菌污染的潜在风险。本研究结果提供了实证证据和潜在的机理解释，即河流水质退化是城市蔓延的结果。最后，机器学习是土地利用规划中情景预测的有力工具，可用于预测不同城市发展模式情景下的环境影响。
