Air Pollution Assessment in China: A Novel Group Multiple Criteria Decision Making Model under Uncertain Information

Random Forests Assessment of the Role of Atmospheric Circulation in PM10 in an Urban Area with Complex Topography

This study presents the assessment of the quantitative influence of atmospheric circulation on the pollutant concentration in the area of Kraków, Southern Poland, for the period 2000–2020. The research has been realized with the application of different statistical parameters, synoptic meteorology tools, the Random Forests machine learning method, and multilinear regression analyses. Another aim of the research was to evaluate the types of atmospheric circulation classification methods used in studies on air pollution dispersion and to assess the possibility of their application in air quality management, including short-term PM10 daily forecasts. During the period analyzed, a significant decreasing trend of pollutants’ concentrations and varying atmospheric circulation conditions was observed. To understand the relation between PM10 concentration and meteorological conditions and their significance, the Random Forests algorithm was applied. Observations from meteorological stations, air quality measurements and ERA-5 reanalysis were used. The meteorological database was used as an input to models that were trained to predict daily PM10 concentration and its day-to-day changes. This study made it possible to distinguish the dominant circulation types with the highest probability of occurrence of poor air quality or a significant improvement in air quality conditions. Apart from the parameters whose significant influence on air quality is well established (air temperature and wind speed at the ground and air temperature gradient), the key factor was also the gradient of relative air humidity and wind shear in the lowest troposphere. Partial dependence calculated with the use of the Random Forests model made it possible to better analyze the impact of individual meteorological parameters on the PM10 daily concentration. The analysis has shown that, for areas with a diversified topography, it is crucial to use the variability of the atmospheric circulation during the day to better forecast air quality.

A Review of Field Measurement Studies on Thermal Comfort, Indoor Air Quality and Virus Risk

People spend up to 90% of their time indoors where they continuously interact with the indoor environment. Indoor Environmental Quality (IEQ), and in particular thermal comfort, Indoor Air Quality (IAQ), and acoustic and visual comfort, have proven to be significant factors that influence the occupants’ health, comfort, productivity and general well-being. The ongoing COVID-19 pandemic has also highlighted the need for real-life experimental data acquired through field measurement studies to help us understand and potentially control the impact of IEQ on the occupants’ health. In this context, there was a significant increase over the past two decades of field measurement studies conducted all over the world that analyse the IEQ in various indoor environments. In this study, an overview of the most important factors that influence the IAQ, thermal comfort, and the risk of virus transmission is first presented, followed by a comprehensive review of selected field measurement studies from the last 20 years. The main objective is to provide a broad overview of the current status of field measurement studies, to identify key characteristics, common outcomes, correlations, insights, as well as gaps, and to serve as the starting point for conducting future field measurement studies.

Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems

Air quality monitoring systems differ in composition and accuracy of observations and their temporal and spatial coverage. A monitoring system’s performance can be assessed by evaluating the accuracy of the emission sources identified by its data. In the considered inverse modeling approach, a source identification problem is transformed to a quasi-linear operator equation with the sensitivity operator. The sensitivity operator is composed of the sensitivity functions evaluated on the adjoint ensemble members. The members correspond to the measurement data element aggregates. Such ensemble construction allows working in a unified way with heterogeneous measurement data in a single-operator equation. The quasi-linear structure of the resulting operator equation allows both solving and predicting solutions of the inverse problem. Numerical experiments for the Baikal region scenario were carried out to compare different types of inverse problem solution accuracy estimates. In the considered scenario, the projection to the orthogonal complement of the sensitivity operator’s kernel allowed predicting the source identification results with the best accuracy compared to the other estimate types. Our contribution is the development and testing of a sensitivity-operator-based set of tools for analyzing heterogeneous air quality monitoring systems. We propose them for assessing and optimizing observational systems and experiments.