The Optimization Strategy of the Existing Urban Green Space Soil Monitoring System in Shanghai, China

Zhang, Weiwei; Han, Jigang; Molla, Abiot; Zuo, Shixiang; Ren, Yin

doi:10.3390/ijerph18094820

Cited by 6 publications

(1 citation statement)

References 81 publications

(99 reference statements)

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“…1) 한국의 주요 도시에서 매년 발생하는 고농도 대기오염은 국민 의 건강상 피해를 일으키며 2,3) , 사회･경제적으로 직간접적인 악영향을 미치고 있다. 4,5) 8,9) , 대기 10,11) , 토양 12,13) 11,14) 또한, 대기오염 배출원과 거리가 가까워 대기오염의 우려가 있어 지속적으로 모니터링이 요망되는 지점에 설치되어야 한다. 15,16) 최종 위치 에 대한 의사결정 시, 지역의 특수한 사회･경제･환경적 조건 들을 고려해야 한다.…”

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Expansion of Roadside Air Pollution Monitoring Network in Seoul using Learning-based Optimization Method

Kim¹,

Shin²,

Kim³

et al. 2023

J Korean Soc Environ Eng

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Objectives : In this study, a learning-based optimization method is proposed and implemented for determining new monitoring sites when expanding the roadside air pollution monitoring network. Utilizing the bigdata available in Seoul, this decision-making tool is developed that takes into account the objectives of selecting new monitoring sites and incorporates social, economic, and environmental characteristics. The optimized results can suggest potential locations for new roadside air pollution monitoring sites. Additionally, the capability of this tool to facilitate objective decision-making processes is evaluated by determining the influence range providing reliable air pollution information with the addition of the new monitoring sites.Methods : The proposed learning-based optimization algorithm is a new approach for selecting the new optimal monitoring sites by comprehensively considering social, economic, and environmental factors aligned with the installation purpose of the monitoring system in Seoul. The algorithm starts with genetic algorithms to select candidate locations for new monitoring sites that maximize the influence area of the expanded monitoring network compared to the existing monitoring network, capture a high overall level of air pollution, and do not overlap with the existing monitoring network. After that, PROMETHEE method is applied to evaluate the solutions generated by the genetic algorithm and choose the final solution that best fits six evaluation factors (Information entropy, number of new monitoring sites, distance from point sources, wind speed, traffic volume, and population) to be considered when installing new monitoring sites.Results and Discussion : The learning-based optimization algorithm selects 10 potential new monitoring sites adding to the existing roadside air pollution monitoring network having 15 monitoring sites. The explainable spatiotemporal range of the air pollution information that can be expected after the installation of the new monitoring sites is quantified to cover 84.33% of Seoul, reducing the uncertainty of the air pollution information of existing monitoring network by 26.15%. The final solution, selected from several solutions, can get new optimal roadside air pollution monitoring sites reflecting the regional characteristics of Seoul and the installation purpose of the monitoring system by having a small number of newly established monitoring locations, being close to air pollution emissions facilities, and having a high population and traffic volume.Conclusion : The proposed learning-based optimization method, using relevant variables for the installation purpose of the monitoring system, can derive the objective solution for deciding new monitoring locations of the roadside air pollution monitoring network, considering additional social factors as opposed to urban air pollution monitoring network. The final solution obtained through the optimization algorithm has great potential for future use, as it can guide to determine practical and feasible new monitoring sites with additional on-site verification. Furthermore, this optimized approach can be applied widely during the decision-making process for the expansion of other environmental monitoring networks.

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Expansion of Roadside Air Pollution Monitoring Network in Seoul using Learning-based Optimization Method

Kim¹,

Shin²,

Kim³

et al. 2023

J Korean Soc Environ Eng

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A machine learning and geostatistical hybrid method to improve spatial prediction accuracy of soil potentially toxic elements

Molla

Zhang

Zuo

et al. 2022

Stoch Environ Res Risk Assess

Self Cite

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Effective environmental management and contamination remediation require accurate spatial variation and prediction of potentially toxic elements (PTEs) in the soil. However, no single method has been developed to predict soil PTEs accurately. This study evaluated the ability of the advanced geostatistical method of empirical Bayesian kriging regression prediction (EBKRP), machine learning algorithms of random forest (RF), and the combination of RF and EBKRP to predict and map soil PTE content. The root mean square error (RMSE), mean absolute percentage error (MAPE), and coe cient of determination (R 2 ) were used to assess model prediction performance. As identi ed by RF, soil organic carbon, soil organic matter, total (nitrogen, phosphorus, and potassium), slope, and elevation were ranked as signi cant covariates to improve the prediction accuracy of PTEs in greenspace soil. Results showed that the RF method improved the prediction accuracy over the EBKRP method, and the improvement was from 24-40% for RMSE, 41-210% for R 2 , and 18-35% for MAPE. However, hybrid methods (RF-EBKRP) increased accuracy by comparing the individually predicted models with 345% and 33% in EBKRP and RF, based on R 2 , respectively. Moreover, the RF-EBKRP method decreased MAPE by 5.33-18.69% and 0.48-12.33% on average than EBKRP and RF, respectively.In conclusion, in addition to incorporating covariates into the models, combining kriging residuals with the machine learning method (RF-EBKRP) resulted in a promising approach for improving the distribution accuracy and mapping of PTEs in the soil.

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Association between residential greenspace and mental health among cancer survivors in Shanghai, China

Li,

Liu,

Song

et al. 2023

Environmental Research

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The Optimization Strategy of the Existing Urban Green Space Soil Monitoring System in Shanghai, China

Cited by 6 publications

References 81 publications

Expansion of Roadside Air Pollution Monitoring Network in Seoul using Learning-based Optimization Method

Expansion of Roadside Air Pollution Monitoring Network in Seoul using Learning-based Optimization Method

A machine learning and geostatistical hybrid method to improve spatial prediction accuracy of soil potentially toxic elements

Association between residential greenspace and mental health among cancer survivors in Shanghai, China

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