Effects of Air Pollution on Assaults: Findings from South Korea

Eum, Jeongin; Kim, Hyungkyoo

doi:10.3390/su132011545

Cited by 3 publications

(1 citation statement)

References 70 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…19) (6) 풍속 풍속이 낮을 경우, 대기 정체로 인해 고농도 대기오염 물질 의 집중 가능성을 증가시키므로 대기오염도가 높다. 44) 측정 후보 그리드와 가장 인접한 기상 측정 지점을 매칭하고 평균 풍속(V s )을 산정한다(Eq. 20).…”

Section:   unclassified

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

View full text Add to dashboard Cite

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.

show abstract

Section:   unclassified

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

View full text Add to dashboard Cite

show abstract

Study on Spatiotemporal Evolution Features and Affecting Factors of Collaborative Governance of Pollution Reduction and Carbon Abatement in Urban Agglomerations of the Yellow River Basin

Yang

Wang

et al. 2023

IJERPH

View full text Add to dashboard Cite

Exploring spatiotemporal evolution features and factors affecting pollution reduction and carbon abatement on the urban agglomeration scale is helpful to better understand the interaction between ecological environment and economic development in urban agglomerations. In this study, we constructed an evaluation index system for collaborative governance of pollution reduction and carbon abatement in urban agglomerations. In addition, we employed the correlation coefficient matrix, the composite system synergy model, the Gini coefficient, and the Theil index to evaluate the level of and regional differences in collaborative governance of pollution reduction and carbon abatement in seven urban agglomerations in the Yellow River Basin from 2006 to 2020. Moreover, we explored the factors affecting collaborative governance of pollution reduction and carbon abatement in urban agglomerations in the basin. The following findings were obtained: (1) the order degree of collaborative governance of pollution reduction and carbon abatement in the seven urban agglomerations exhibited a significant growing trend, representing a spatial evolution feature of “high in the west and low in the east”; (2) the internal differences in collaborative governance synergy of pollution reduction and carbon abatement decreased in Lanzhou–Xining Urban Agglomeration, Hohhot–Baotou–Ordos–Yulin Urban Agglomeration, Central Shanxi Urban Agglomeration, Zhongyuan Urban Agglomeration, and Shandong Peninsula Urban Agglomeration, while the internal differences basically remained stable in Guanzhong Urban Agglomeration and the Urban Agglomeration along the Yellow River in Ningxia; (3) the variances in environmental regulation and industrial structure among urban agglomerations had a significant positive effect on collaborative governance of pollution reduction and carbon abatement in urban agglomerations in the basin, and the variances in economic growth had a significant inhibitory effect. In addition, the variances in energy consumption, greening construction, and opening-up had an inhibitory impact on collaborative governance of pollution reduction, but the impact was not significant. Finally, this study proposes various recommendations to improve collaborative governance for pollution reduction and carbon abatement in urban agglomerations in the basin in terms of promoting industrial structure upgrading, strengthening regional cooperation, and reducing regional differences. This paper represents an empirical reference for formulating differentiated collaborative governance strategies for pollution reduction and carbon abatement, comprehensive green and low-carbon economic and social transformation programs, and high-quality green development paths in urban agglomerations, which is of certain theoretical and practical significance.

show abstract

Forecasting Daytime Ground-Level Ozone Concentration in Urbanized Areas of Malaysia Using Predictive Models

et al. 2022

View full text Add to dashboard Cite

Ground-level ozone (O3) is one of the most significant forms of air pollution around the world due to its ability to cause adverse effects on human health and environment. Understanding the variation and association of O3 level with its precursors and weather parameters is important for developing precise forecasting models that are needed for mitigation planning and early warning purposes. In this study, hourly air pollution data (O3, CO, NO2, PM10, NmHC, SO2) and weather parameters (relative humidity, temperature, UVB, wind speed and wind direction) covering a ten year period (2003–2012) in the selected urban areas in Malaysia were analyzed. The main aim of this research was to model O3 level in the band of greatest solar radiation with its precursors and meteorology parameters using the proposed predictive models. Six predictive models were developed which are Multiple Linear Regression (MLR), Feed-Forward Neural Network (FFANN), Radial Basis Function (RBFANN), and the three modified models, namely Principal Component Regression (PCR), PCA-FFANN, and PCA-RBFANN. The performances of the models were evaluated using four performance measures, i.e., Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Index of Agreement (IA), and Coefficient of Determination (R2). Surface O3 level was best described using linear regression model (MLR) with the smallest calculated error (MAE = 6.06; RMSE = 7.77) and the highest value of IA and R2 (0.85 and 0.91 respectively). The non-linear models (FFANN and RBFANN) fitted the observed O3 level well, but were slightly less accurate compared to MLR. Nonetheless, all the unmodified models (MLR, ANN, and RBF) outperformed the modified-version models (PCR, PCA-FFANN, and PCA-RBFANN). Verification of the best model (MLR) was done using air pollutant data in 2018. The MLR model fitted the dataset of 2018 very well in predicting the daily O3 level in the specified selected areas with the range of R2 values of 0.85 to 0.95. These indicate that MLR can be used as one of the reliable methods to predict daytime O3 level in Malaysia. Thus, it can be used as a predictive tool by the authority to forecast high ozone concentration in providing early warning to the population.

show abstract

Effects of Air Pollution on Assaults: Findings from South Korea

Cited by 3 publications

References 70 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

Study on Spatiotemporal Evolution Features and Affecting Factors of Collaborative Governance of Pollution Reduction and Carbon Abatement in Urban Agglomerations of the Yellow River Basin

Forecasting Daytime Ground-Level Ozone Concentration in Urbanized Areas of Malaysia Using Predictive Models

Contact Info

Product

Resources

About