Statistical Approaches for Forecasting Primary Air Pollutants: A Review

Abstract

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.

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

“…As stated previously, seven methods were used to predict the PM 2.5 concentration. A regression-based method-i.e., autoregressive integrated moving average (ARIMA)-was applied in this study because it has been extensively applied in previous research on air pollution forecasting [24]. However, the ARIMA model does not have the capacity to capture nonlinear relationships.…”

Modelling the Interaction between Air Pollutant Emissions and Their Key Sources in Poland

“…The presented experiments show that quite accurate predictions of air pollutant indicator levels are possible with a simple NN. In a paper [37], the outcomes indicated that researchers mainly focused on the effects of air pollution on human diseases, urban pollution exposure models, and land use regression (LUR) techniques. O 3 , NO x and PM were the most tested contaminants.…”

“…The results [37] show that the ANN method became an increasingly popular means to predict air pollution, and the interaction between different pollutants attracted increasing research attention, with PM + NO x and PM + O 3 being the main combinations. Furthermore, the results indicate that since 2010, hybrid methods have rapidly joined the mainstream of air-pollution prediction and have been the fastest-developing research method through Markov chain analysis.…”

“…In the future, the statistical air-pollution prediction is expected to be based on a mixed approach to predict multiple pollutants simultaneously, and the interaction between them will be the most challenging aspect of research on air-pollution forecasting [37].…”

New Technologies to Decontaminate Pollutants in Water: A Report about the State of the Art

“…The production of food and all types of utility is part of a cyclical mechanism that involves the consumption of raw materials and the consequential production of waste that must be disposed of or recycled. Unfortunately, to this day the quantity of products that are not recycled because they end up in the environment is conspicuous [ 1 , 2 ]. The cause of the contamination of our planet is manifold and the pharmaceutical, agro-food, construction and energy industries are among the many examples [ 3 ].…”