The COVID-19 restrictions in 2020 have led to distinct variations in NO₂and O₃concentrations in China. Here, the different drivers of anthropogenic emission changes, including the effects of the Chinese New Year (CNY), China’s 2018–2020 Clean Air Plan (CAP), and the COVID-19 lockdown and their impact on NO₂and O₃are isolated by using a combined model-measurement approach. In addition, the contribution of prevailing meteorological conditions to the concentration changes was evaluated by applying a machine-learning method. The resulting impact on the multi-pollutant Health-based Air Quality Index (HAQI) is quantified. The results show that the CNY reduces NO₂concentrations on average by 26.7% each year, while the COVID-lockdown measures have led to an additional 11.6% reduction in 2020, and the CAP over 2018–2020 to a reduction in NO₂by 15.7%. On the other hand, meteorological conditions from 23 January to March 7, 2020 led to increase in NO₂of 7.8%. Neglecting the CAP and meteorological drivers thus leads to an overestimate and underestimate of the effect of the COVID-lockdown on NO₂reductions, respectively. For O₃the opposite behavior is found, with changes of +23.3%, +21.0%, +4.9%, and −0.9% for CNY, COVID-lockdown, CAP, and meteorology effects, respectively. The total effects of these drivers show a drastic reduction in multi-air pollutant-related health risk across China, with meteorology affecting particularly the Northeast of China adversely. Importantly, the CAP’s contribution highlights the effectiveness of the Chinese government’s air-quality regulations on NO₂reduction.

Air pollution is one of the leading causes for global deaths and understanding pollutant emission sources is key to successful mitigation policies. Air quality data in the urban, suburban, industrial, and rural areas (UA, SA, IA, and RA) of Jining, Shandong Province in China, were collected to compare the characteristics and associated health risks. The average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, and CO show differences of −3.87, −16.67, −19.24, −15.74, and −8.37% between 2017 and 2018. On the contrary, O₃ concentrations increased by 4.50%. The four functional areas exhibited the same seasonal variations and diurnal patterns in air pollutants, with the highest exposure excess risks (ERs) resulting from O₃. More frequent ER days occurred within the 25–30°C, but much larger ERs are found within the 0–5°C temperature range, attributed to higher O₃ pollution in summer and more severe PM pollution in winter. The premature deaths attributable to six air pollutants can be calculated in 2017 and 2018, respectively. Investigations on the potential source show that the ER of O₃ (r of 0.86) had the tightest association with the total ER. The bivariate polar plots indicated that the highest health-based air quality index (HAQI) in IA influences the HAQI in UA and SA by pollution transport, and thus can be regarded as the major pollutant emission source in Jining. The above results indicate that urgent measures should be taken to reduce O₃ pollution taking into account the characteristics of the prevalent ozone formation regime, especially in IA in Jining.

Using the temperature data observed from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER), we investigate the response of the mesosphere and lower thermosphere (MLT) to two medium geomagnetic storms with disparate durations, on 20 April 2018 and 10 April 2022. The high-latitude MLT temperature increase in the Southern hemisphere can reach 40 K during April 2018 geomagnetic storm with a longer duration (Kp values greater than 4 for 15 h), while the temperature variations are less than 10 K for the April 2022 event (Kp values greater than 4 for 6 h). To investigate the different temperature responses to disparate geomagnetic storm durations and understand what physical process results in this difference, we simulated the two events using the thermosphere ionosphere mesosphere electrodynamics general circulation model (TIMEGCM). The simulations show that more particles and energy input in longer-duration geomagnetic storms produce larger ion drag force and pressure gradient force at ~130 km, and then the enhanced two forces cause faster horizontal wind, leading to larger horizontal divergence. Subsequently, the stronger downward vertical wind is transported to the MLT region (below 110 km) and ultimately makes greater temperature increases through adiabatic heating/cooling and vertical advection. Therefore, the effects of the storm’s duration on the MLT temperature are also important.