Atmospheric trace metals entail significant damages in human health and ecosystem safety, and thus a series of clean air actions have been implemented to decrease the ambient element concentrations. Unfortunately, the impact of these emission control measures on element concentrations in fine particles remains poorly understood. In our study, the random forest (RF) model was applied to distinguish the effects of emission and meteorology to trace elements in PM_2.5 in a typical industrial city named Tangshan based on a 3-year (2017–2020) hourly field observation. The result suggested that the clean air actions have facilitated the dramatic decreases of the deweathered concentrations of Ga, Co, Pb, Zn, and As by 72 %, 67 %, 62 %, 59 %, and 54 %, respectively. It is attributable to the strict implementation of “coal to gas” strategies and optimisation of industrial structure and layout. However, the deweathered levels of Ca (8.3 %), Cr (18.5 %), and Fe (23 %) only displayed minor decreases, indicating that the emission control measures for ferrous metal smelting and vehicle emission were not very effective. The positive matrix factorisation (PMF) results suggested that the contribution ratios of biomass burning, non-ferrous metal smelting, coal combustion, ferrous metal smelting, heavy oil combustion, and traffic-related dust changed from 33 %, 11 %, 15 %, 13 %, 3 %, and 25 % to 33 %, 8 %, 8 %, 13 %, 4 %, and 33 %, respectively. To date, no significant non-carcinogenic and carcinogenic risks were observed for all of the elements, while both of As and Pb still showed relatively high health damages. It was proposed to further cut down the combustion-related emissions (e.g. As and Pb) because it showed the highest marginal health benefits. Besides this, the control of traffic-related emissions might be a key abatement strategy to facilitate the reduction of elements in fine particles.

High contents of reactive nitrogen components aggravate air pollution and could also impact ecosystem structures and functioning across the terrestrial–aquatic–marine continuum. However, the long-term historical trends and future predictions of reactive nitrogen components at the global scale still remain highly uncertain. In our study, field observations, satellite products, model outputs, and many other covariates were integrated into the multi-stage machine-learning model to capture the global patterns of reactive nitrogen components during 2000–2019. In order to decrease the estimate uncertainties in the future scenarios, the constructed reactive nitrogen component dataset for the historical period was utilised as the constraint to calibrate the CMIP6 dataset in four scenarios. The results suggested that the cross-validation (CV) R² values of four species showed satisfying performance (R²>0.55). The concentrations of estimated reactive nitrogen components in China experienced persistent increases during 2000–2013, while they suffered drastic decreases from 2013, except for NH₃. This might be associated with the impact of clean-air policies. However, in Europe and the United States, these compounds have remained relatively stable since 2000. In the future scenarios, SSP3-7.0 (traditional-energy scenario) and SSP1-2.6 (carbon neutrality scenario) showed the highest and lowest reactive nitrogen component concentrations, respectively. Although the reactive nitrogen concentrations in some heavy-pollution scenarios (SSP3-7.0) also experienced decreases during 2020–2100, SSP1-2.6 and SSP2-4.5 (middle-emission scenario) still showed more rapidly decreasing trends. Our results emphasise the need for carbon neutrality pathways to reduce global atmospheric N pollution.