A review on characteristics and mitigation strategies of indoor air quality in underground subway stations.

Due to the rapidly increasing ridership and the relatively enclosed underground space, the indoor air quality (IAQ) in underground subway stations (USSs) has attracted more public attention. The air pollutants in USSs, such as particulate matter (PM), CO2 and volatile organic compounds (VOCs), are hazardous to the health of passengers and staves. Firstly, this paper presents a systematic review on the characteristics and sources of air pollutants in USSs. According to the review work, the concentrations of PM, CO2, VOCs, bacteria and fungi in USSs are 1.1-13.2 times higher than the permissible concentration limits specified by WHO, ASHRAE and US EPA. The PM and VOCs are mainly derived from the internal and outdoor sources. CO2 concentrations are highly correlated with the passenger density and the ventilation rate while the exposure levels of bacteria and fungi depend on the thermal conditions and the settled dust. Then, the online monitoring, fault detection and prediction methods of IAQ are summarized and the advantages and disadvantages of these methods are also discussed. In addition, the available control strategies for improving IAQ in USSs are reviewed, and these strategies are classified and compared from different viewpoints. Lastly, challenges of the IAQ management in the context of the COVID-19 epidemic and several suggestions for underground stations' IAQ management in the future are put forward. This paper is expected to provide a comprehensive guidance for further research and design of the effective prevention measures on air pollutants in USSs so as to achieve more sustainable and healthy underground environment.

Characteristics prediction of hydrothermal biochar using data enhanced interpretable machine learning.

Hydrothermal biochar is a promising sustainable soil remediation agent for plant growth. Demands for biochar properties differ due to the diversity of soil environment. In order to achieve accurate biochar properties prediction and overcome the interpretability bottleneck of machine learning models, this study established a series of data-enhanced machine learning models and conducted relevant sensitivity analysis. Compared with traditional support vector machine, artificial neural network, and random forest models, the accuracy after data enhancement increased in average from 5.8% to 15.8%, where the optimal random forest model showed the average of accuracy was 94.89%. According to sensitivity analysis results, the essential factors influencing the predicting results of the models were reaction temperature, reaction pressure, and specific element of biomass feedstock. As a result, data-enhanced interpretable machine learning proved promising for the characteristics prediction of hydrothermal biochar.

Investigating the environmental quality deterioration and human health hazard caused by heating emissions.

By analyzing the status quo of the district heating energy consumption and air quality in China, it was found that with the environmental quality degradation, the previous assessment method using energy efficiency as the index cannot consider the impact on the environmental quality and human health simultaneously. Hence, to analyze the environmental quality deterioration and human health hazard due to the heating emissions, a novel assessment method was proposed and can be used to guide the improvement strategies. In this study, based on the quantitative influence of the heating emissions, urban population density and pollution duration, heating impact index (HII) as a new comprehensive evaluation index was introduced. It can be used to assess the urgency of the heating improvement in the studied regions. To estimate the air pollution status quo caused by heating emissions, 66 main heating cities in China were selected as the studied objects. The results showed that the calculated average heating emission-contributed air quality index (AQI) was 45% and polluted time percentage was 39%. The industrial emissions in 19 cities were the main reason to deteriorate the environmental quality during the heating season. While in the remaining 47 cities (71.2% of total studied cities), heating emissions dominated the deterioration of environmental quality. Subsequently, the cities with HII larger than 0.1 were divided into six levels to provide a guide for the Chinese government and relevant departments to improve the heating system and reduce human health hazard. This study can also be used as a reference for other polluted cities to investigate the environmental quality deterioration and human health hazard degrees due to the heating emissions.