Air pollutant forecasting can be used to quantitatively estimate pollutant reduction trends. Combining bibliometrics with the evolutionary tree and Markov chain methods can achieve a superior quantitative analysis of research hotspots and trends. In this work, we adopted a bibliometric method to review the research status of statistical prediction methods for air pollution, used evolutionary trees to analyze the development trend of such research, and applied the Markov chain to predict future research trends for major air pollutants. The results indicate that papers mainly focused on the effects of air pollution on human diseases, urban pollution exposure models, and land use regression (LUR) methods. Particulate matter (PM), nitrogen oxides (NOx), and ozone (O3) were the most investigated pollutants. Artificial neural network (ANN) methods were preferred in studies of PM and O3, while LUR were more widely used in studies of NOx. Additionally, multi-method hybrid techniques gradually became the most widely used approach between 2010 and 2018. In the future, the statistical prediction of air pollution is expected to be based on a mixed method to simultaneously predict multiple pollutants, and the interaction between pollutants will be the most challenging aspect of research on air pollution prediction. The research results summarized in this paper provide technical support for the accurate prediction of atmospheric pollution and the emergency management of regional air quality.

Statistical Approaches for Forecasting Primary Air Pollutants: A Review

Using indoor plants as biomonitors for detection of toxic metals by tobacco smoke

Context: Ozone concentrations near the land surface are rising in Asia while they are declining or stagnating in Europe and North America. Ozone is the most widespread air pollutant negatively affe...

https://www.tandfonline.com/doi/pdf/10.1080/20964129.2021.1911602

Emerging challenges of ozone impacts on asian plants: actions are needed to protect ecosystem health

Exposure of humans to high concentrations of PM2.5 has adverse effects on their health. Researchers estimate that exposure to particulate matter from fossil fuel emissions accounted for 18% of deaths in 2018—a challenge policymakers argue is being exacerbated by the increase in the number of extreme weather events and rapid urbanization as they tinker with strategies for reducing air pollutants. Drawing on a number of ensemble machine learning methods that have emerged as a result of advancements in data science, this study examines the effectiveness of using ensemble models for forecasting the concentrations of air pollutants, using PM2.5 as a representative case. A comprehensive evaluation of the ensemble methods was carried out by comparing their predictive performance with that of other standalone algorithms. The findings suggest that hybrid models provide useful tools for PM2.5 concentration forecasting. The developed models show that machine learning models are efficient in predicting air particulate concentrations, and can be used for air pollution forecasting. This study also provides insights into how climatic factors influence the concentrations of pollutants found in the air.

/pdf/modelling-and-forecasting-temporal-pm2-5-concentration-using-2u1ysv52.pdf

Modelling and Forecasting Temporal PM2.5 Concentration Using Ensemble Machine Learning Methods

Abstract Soil viruses remain understudied when compared to virus found in aquatic ecosystems. Here, we investigate the ecological patterns of soil viral communities across various land use types encompassing forest, agricultural, and urban soil in Xiamen, China. We recovered 59,626 viral operational taxonomic units (vOTUs) via size-fractioned viromic approach with additional mitomycin C treatment to induce virus release from bacterial fraction. Our results show that viral communities are significantly different amongst the land use types considered. A microdiversity analysis indicates that selection act on soil vOTUs, resulting in disparities between land use associated viral communities. Soil pH is one of the major determinants of viral community structure, associated with changes of in-silico predicted host compositions of soil vOTUs. Habitat disturbance and variation of soil moisture potentially contribute to the dynamics of putative lysogenic vOTUs. These findings provide mechanistic understandings of the ecology and evolution of soil viral communities in changing environments. 

/pdf/response-of-soil-viral-communities-to-land-use-changes-1hxespmm.pdf

Response of soil viral communities to land use changes

https://iopscience.iop.org/article/10.1088/1748-9326/abcee9/pdf

A review of research hotspots and trends in biogenic volatile organic compounds (BVOCs) emissions combining bibliometrics with evolution tree methods

Metagenomic and viromic analysis reveal the anthropogenic impacts on the plasmid and phage borne transferable resistome in soil.

Volatile organic compound (VOC) emissions have attracted wide attention due to their impacts on atmospheric quality and public health. However, most studies reviewed certain aspects of natural VOCs (NVOCs) or anthropogenic VOCs (AVOCs) rather than comprehensively quantifying the hotspots and evolution trends of AVOCs and NVOCs. We combined the bibliometric method with the evolution tree and Markov chain to identify research focus and uncover the trends in VOC emission sources. This study found that research mainly focused on VOC emission characteristics, effects on air quality and health, and VOC emissions under climate change. More studies concerned on AVOCs than on NVOCs, and AVOC emissions have shifted with a decreasing proportion of transport emissions and an increasing share of solvent utilization in countries with high emissions and publications (China and the USA). Research on AVOCs is imperative to develop efficient and economical abatement techniques specific to solvent sources or BTEX species to mitigate the detrimental effects. Research on NVOCs originating from human sources risen due to their application in medicine, while studies on sources sensitive to climate change grew slowly, including plants, biomass burning, microbes, soil and oceans. Research on the long-term responses of NVOCs derived from various sources to climate warming is warranted to explore the evolution of emissions and the feedback on global climate. It is worthwhile to establish an emission inventory with all kinds of sources, accurate estimation, high spatial and temporal resolution to capture the emission trends in the synergy of industrialization and climate change as well as to simulate the effects on air quality. We review VOC emissions from both anthropogenic and natural sources under climate change and their effects on atmospheric quality and health to point out the research directions for the comprehensive control of global VOCs and mitigation of O3 pollution. 

Chensong Duan

Papers

Statistical Approaches for Forecasting Primary Air Pollutants: A Review

A review of research hotspots and trends in biogenic volatile organic compounds (BVOCs) emissions combining bibliometrics with evolution tree methods

Response of soil viral communities to land use changes

Metagenomic and viromic analysis reveal the anthropogenic impacts on the plasmid and phage borne transferable resistome in soil.

The research hotspots and trends of volatile organic compound emissions from anthropogenic and natural sources: A systematic quantitative review.