A methodological framework for improving air quality monitoring network layout. Applications to environment management

Elucidating Best Geospatial Estimation Method Applied to Environmental Sciences

The aim of this study is to assess and identify the most suitable geospatial interpolation algorithm for environmental sciences. The research focuses on evaluating six different interpolation methods using annual average PM10 concentrations as a reference dataset. The dataset includes measurements obtained from a target air quality network (scenery 1) and a sub-dataset derived from a partitive clustering technique (scenery 2). By comparing the performance of each interpolation algorithm using various indicators, the study aims to determine the most reliable method. The findings reveal that the kriging method demonstrates the highest performance within environmental sciences, with a spatial similarity of approximately 70% between the two scenery datasets. The performance indicators for the kriging method, including RMSE (root mean square error), MAE (mean absolute error), and MAPE (mean absolute percentage error), are measured at 3.2 µg/m3, 10.2 µg/m3, and 7.3%, respectively.This study addresses the existing gap in scientific knowledge regarding the comparison of geospatial interpolation techniques. The findings provide valuable insights for environmental managers and decision-makers, enabling them to implement effective control and mitigation strategies based on reliable geospatial information and data. In summary, this research evaluates and identifies the most suitable geospatial interpolation algorithm for environmental sciences, with the kriging method emerging as the most reliable option. The study's findings contribute to the advancement of knowledge in the field and offer practical implications for environmental management and planning.

Entropy-based air quality monitoring network optimization using NINP and Bayesian maximum entropy

Effectual air quality monitoring network (AQMN) design plays a prominent role in environmental engineering. An optimal AQMN design should consider stations' mutual information and system uncertainties for effectiveness. This study develops a novel optimization model using a non-dominated sorting genetic algorithm II (NSGA-II). The Bayesian maximum entropy (BME) method generates potential stations as the input of a framework based on the transinformation entropy (TE) method to maximize the coverage and minimize the probability of selecting stations. Also, the fuzzy degree of membership and the nonlinear interval number programming (NINP) approaches are used to survey the uncertainty of the joint information. To obtain the best Pareto optimal solution of the AQMN characterization, a robust ranking technique, called Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) approach, is utilized to select the most appropriate AQMN properties. This methodology is applied to Los Angeles, Long Beach, and Anaheim in California, USA. Results suggest using 4, 4, and 5 stations to monitor CO, NO₂, and ozone, respectively; however, implementing this recommendation reduces coverage by 3.75, 3.75, and 3 times for CO, NO₂, and ozone, respectively. On the positive side, this substantially decreases TE for CO, NO₂, and ozone concentrations by 8.25, 5.86, and 4.75 times, respectively.

Urban air quality changes resulting from the lockdown period due to the COVID-19 pandemic

This work aims to quantify potential pollution level changes in an urban environment (Madrid city, Spain) located in South Europe due to the lockdown measures for preventing the SARS-CoV-2 transmission. Polluting 11 species commonly monitored in urban zones were attended. Except for O3, a prompt target pollutant levels abatement was reached, intensely when implanted stricter measures and moderately along those measures' relaxing period. In the case of TH and CH4, it is evidenced a progressive diminution over the lockdown period. While the highest decreasing average changes relapsed on NOx (NO2: - 40.0% and NO: - 33.3%) and VOCs (C7H8: - 36.3% and C6H6: - 32.8%), followed by SO2 (- 27.0%), PM10 (- 19.7%), CO (- 16.6%), CH4 (- 14.7%), TH (- 11.6%) and PM2.5 (- 10.1%), the O3 level slightly raised 0.4%. These changes were consistently dependent on the measurement station location, emphasizing urban background zones for SO2, CO, C6H6, C7H8, TH and CH4, suburban zones for PM2.5 and O3, urban traffic sites for NO and PM10, and keeping variations reasonably similar at all the stations in the case of NO2. Those pollution changes were not translated in variations on geospatial pattern, except for NO, O3 and SO2. Although the researched urban atmosphere improvement was not attributable to meteorological conditions' variations, it was in line with the decline in traffic intensity. The evidenced outcomes might offer valuable clues to air quality managers in urban environments regarding decision-making in favor of applying punctual severe measures for quickly and considerably relieving polluting high load occurred in urban environments.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13762-022-04464-6.

A novel assessment framework for improving air quality monitoring network layout

Redundant stations in the air quality monitoring network (AQMN), not only cause high maintenance and operation costs, but also affect the performance of air quality assessment. This study presents a novel framework for identifying the redundant stations and selecting the corresponding alternatives in AQMN. The framework composes three main steps. Firstly, we identify the redundant stations by correlation analysis and stepwise regression methods. Secondly, we determine the corresponding alternative stations by cluster analysis and correspondence analysis methods. Finally, the final optimization results are verified by the support vector regression. We perform empirical evaluations of the framework using Shanghai's AQMN. The results show that Xuhui, Zhangjiang, Shiwuchang, and Pudong New Area are four redundant pollution monitoring stations. Alternatives for each type of pollutant for these redundant stations are proposed and the adjusted layout of AQMN is verified with historical data. The framework proposed in this study can effectively improve the layout of AQMN, which could be applied to other cities or regions to improve the integrity of pollution information and reduce the monitoring costs.Implications: In this study, we set up a comprehensive framework. A case study proves that the framework we proposed can help countries identify redundant stations, so as to reduce the monitoring costs, improve the monitoring efficiency, and provide technical support for governments to implement accurate air quality control measures.Four particularly important aspects were highlighted in this work: (i) A new framework was constructed that combined regression and prediction for the first time to analyze and validate pollutant data; (ii) The framework used Stepwise Regression to improve previous methods for identifying redundant monitoring stations, effectively improving identification efficiency; (iii) The framework used Support Vector Regression to make predictions to verify the final results of the optimized layout, which was ignored in previous studies. (iv) This framework can be applied to any city or region, which has important practical significance for improving the comprehensiveness and accuracy of pollution monitoring in various cities.