New Insights into Unexpected Severe PM2.5 Pollution during the SARS and COVID-19 Pandemic Periods in Beijing

A new ChatGPT-empowered, easy-to-use machine learning paradigm for environmental science

The quantity and complexity of environmental data show exponential growth in recent years. High-quality big data analysis is critical for performing a sophisticated characterization of the complex network of environmental pollution. Machine learning (ML) has been employed as a powerful tool for decoupling the complexities of environmental big data based on its remarkable fitting ability. Yet, due to the knowledge gap across different subjects, ML concepts and algorithms have not been well-popularized among researchers in environmental sustainability. In this context, we introduce a new research paradigm-"ChatGPT + ML + Environment", providing an unprecedented chance for environmental researchers to reduce the difficulty of using ML models. For instance, each step involved in applying ML models to environmental sustainability, including data preparation, model selection and construction, model training and evaluation, and hyper-parameter optimization, can be easily performed with guidance from ChatGPT. We also discuss the challenges and limitations of using this research paradigm in the field of environmental sustainability. Furthermore, we highlight the importance of "secondary training" for future application of "ChatGPT + ML + Environment".

A machine learning-based study on the impact of COVID-19 on three kinds of pollution in Beijing-Tianjin-Hebei region

Large-scale restrictions on anthropogenic activities in China in 2020 due to the Corona Virus Disease 2019 (COVID-19) indirectly led to improvements in air quality. Previous studies have paid little attention to the changes in nitrogen dioxide (NO2), fine particulate matter (PM2.5) and ozone (O3) concentrations at different levels of anthropogenic activity limitation and their interactions. In this study, machine learning models were used to simulate the concentrations of three pollutants during periods of different levels of lockdown, and compare them with observations during the same period. The results show that the difference between the simulated and observed values of NO2 concentrations varies at different stages of the lockdown. Variation between simulated and observed O3 and PM2.5 concentrations were less distinct at different stages of lockdowns. During the most severe period of the lockdowns, NO2 concentrations decreased significantly with a maximum decrease of 65.28 %, and O3 concentrations increased with a maximum increase of 75.69 %. During the first two weeks of the lockdown, the titration reaction in the atmosphere was disrupted due to the rapid decrease in NO2 concentrations, leading to the redistribution of Ox (NO2 + O3) in the atmosphere and eventually to the production of O3 and secondary PM2.5. The effect of traffic restrictions on the reduction of NO2 concentrations is significant. However, it is also important to consider the increase in O3 due to the constant volatile organic compounds (VOCs) and the decrease in NOx (NO+NO2). Traffic restrictions had a limited effect on improving PM2.5 pollution, so other beneficial measures were needed to sustainably reduce particulate matter pollution. Research on COVID-19 could provide new insights into future clean air action.

Dynamic landscape of multi-elements in PM2.5 revealed by real-time analysis

Metal components in fine particulate matter (PM_2.5) are closely associated with many adverse health outcomes. Dynamic changes of metals in PM_2.5 are critical for risk assessment due to their temporal variations. Herein, an online method for real-time determination of multi-elements (As, Cd, Cs, Cu, Fe, Mg, Mn, Pb, Rb, Sn, Tl, and V) in PM_2.5 was established by directly introducing air samples into inductively coupled plasma mass spectrometry (ICPMS). Meanwhile, a quantified method using metal standard aerosols (Cr, Mo, and W) and high time resolution for 3.3 min online measurement was developed and validated. The limits of detection were in the range of 0.001-6.30 ng/m³ for different metals. Subsequently, the real-time contents of multi-elements in PM_2.5 for 12 h over 33 days were measured at different air qualities. Temporal variations of crustal elements like Fe, Mg are similar to PM_2.5, whereas toxic elements (Pb, As and Cd) have upward trends at dusk. This denoted the association with various emission sources and different exposure concentrations of metals. In addition to the acquisition of real-time information, online analysis of multi-elements in PM_2.5 is beneficial for atmospheric monitoring and provides critical insights into the different exposure risks of metals in PM_2.5 at varying times.

Nonlinear influence of winter meteorology and precursor on PM2.5 based on mathematical and numerical models: A COVID-19 and Winter Olympics case study

Air pollution during the COVID-19 epidemic in Beijing and its surrounding regions has received substantial attention. We collected observational data, including air pollutant concentrations and meteorological parameters, during January and February from 2018 to 2021. A statistical and a numerical model were applied to identify the formation of air pollution and the impact of emission reduction on air quality. Relative humidity, wind speed, SO₂, NO₂, and O₃ had nonlinear effects on the PM_2.5 concentration in Beijing, among which the effects of relative humidity, NO₂, and O₃ were prominent. During the 2020 epidemic period, high pollution concentrations were closely related to adverse meteorological conditions, with different parameters having different effects on the three pollution processes. In general, the unexpected reduction of anthropogenic emissions reduced the PM_2.5 concentration, but led to an increase in the O₃ concentration. Multi-scenario simulation results showed that anthropogenic emission reduction could reduce the average PM_2.5 concentration after the Chinese Spring Festival, but improvement during days with heavy pollution was limited. Considering that O₃ enhances the PM_2.5 levels, to achieve the collaborative improvement of PM_2.5 and O₃ concentrations, further research should explore the collaborative emission reduction scheme with VOCs and NO_x to achieve the collaborative improvement of PM_2.5 and O₃ concentrations. The conclusions of this study provide a basis for designing a plan that guarantees improved air quality for the 2022 Winter Olympics and other international major events in Beijing.