Statistical significance of PM2.5 and O3 trends in China under long-term memory effects

Over the past decade, the Chinese government has implemented the "Clean Air Action" measures to enhance the atmospheric environmental quality, primarily focusing on curbing PM2.5 and O3 concentrations. The efficacy of these strategies and the underlying causes (human factors or natural variability) of any observed increases or decreases in PM2.5 and O3 concentrations are of great importance. Examining the hourly PM2.5 and O3 concentration time series from six representative regions in China between 2015 and 2021 revealed an overall downward trend in PM2.5 concentrations. However, the O3 concentration time series indicated upward trends in some regions, except for the Northeast area (NE) and Sichuan Basin (SCB). In the context of conventional significance tests, the assumption is typically that the time series' samples are independent and therefore memoryless. However, in situations where the time series exhibits strong autocorrelation and limited sample size, this assumption can lead to an overestimation of the statistical significance of the linear trend. To account for this, we utilized a long-term memory model that can reproduce the long-term persistence of pollutant records to improve the accuracy of significance tests. By comparing the P-values of real and surrogate data generated by the long-term memory model, we found that only PM2.5 concentrations in the Pearl River Delta (PRD) were slightly insignificant. For the remaining five regions, the P-values of PM2.5 concentrations were smaller than the significant level of 0.05, suggesting that the observed downward trends in PM2.5 concentrations are not due to natural variability, thereby confirming the effectiveness of the government's policies aimed at curbing atmospheric particulate matter in recent years. Our results show that O3 pollution is significantly increasing only in the Beijing-Tianjin-Hebei (BTH) region, beyond natural variability. In contrast, the trends of O3 pollution in many regions of China are markedly impacted by natural and climate variability.

Rapid Estimation of Climate-Air Quality Interactions in Integrated Assessment Using a Response Surface Model

Air quality and climate change are substantial and linked sustainability challenges, and there is a need for improved tools to assess the implications of addressing these challenges together. Due to the high computational cost of accurately assessing these challenges, integrated assessment models (IAMs) used in policy development often use global- or regional-scale marginal response factors to calculate air quality impacts of climate scenarios. We bridge the gap between IAMs and high-fidelity simulation by developing a computationally efficient approach to quantify how combined climate and air quality interventions affect air quality outcomes, including capturing spatial heterogeneity and complex atmospheric chemistry. We fit individual response surfaces to high-fidelity model simulation output for 1525 locations worldwide under a variety of perturbation scenarios. Our approach captures known differences in atmospheric chemical regimes and can be straightforwardly implemented in IAMs, enabling researchers to rapidly estimate how air quality in different locations and related equity-based metrics will respond to large-scale changes in emission policy. We find that the sensitivity of air quality to climate change and air pollutant emission reductions differs in sign and magnitude by region, suggesting that calculations of "co-benefits" of climate policy that do not account for the existence of simultaneous air quality interventions can lead to inaccurate conclusions. Although reductions in global mean temperature are effective in improving air quality in many locations and sometimes yield compounding benefits, we show that the air quality impact of climate policy depends on air quality precursor emission stringency. Our approach can be extended to include results from higher-resolution modeling and also to incorporate other interventions toward sustainable development that interact with climate action and have spatially distributed equity dimensions.

Particulate matter pollution over China and the effects of control policies

China is one of the regions with highest PM_2.5 concentration in the world. In this study, we review the spatio-temporal distribution of PM_2.5 mass concentration and components in China and the effect of control measures on PM_2.5 concentrations. Annual averaged PM_2.5 concentrations in Central-Eastern China reached over 100μgm^-3, in some regions even over 150μgm^-3. In 2013, only 4.1% of the cities attained the annual average standard of 35μgm^-3. Aitken mode particles tend to dominate the total particle number concentration. Depending on the location and time of the year, new particle formation (NPF) has been observed to take place between about 10 and 60% of the days. In most locations, NPF was less frequent at high PM mass loadings. The secondary inorganic particles (i.e., sulfate, nitrate and ammonium) ranked the highest fraction among the PM_2.5 species, followed by organic matters (OM), crustal species and element carbon (EC), which accounted for 6-50%, 15-51%, 5-41% and 2-12% of PM_2.5, respectively. In response to serious particulate matter pollution, China has taken aggressive steps to improve air quality in the last decade. As a result, the national emissions of primary PM_2.5, sulfur dioxide (SO₂), and nitrogen oxides (NO_X) have been decreasing since 2005, 2006, and 2011, respectively. The emission control policies implemented in the last decade could result in noticeable reduction in PM_2.5 concentrations, contributing to the decreasing PM_2.5 trends observed in Beijing, Shanghai, and Guangzhou. However, the control policies issued before 2010 are insufficient to improve PM_2.5 air quality notably in future. An optimal mix of energy-saving and end-of-pipe control measures should be implemented, more ambitious control policies for NMVOC and NH₃ should be enforced, and special control measures in winter should be applied. 40-70% emissions should be cut off to attain PM_2.5 standard.

Effect of aerosol vertical distribution on aerosol-radiation interaction: A theoretical prospect

This study presents a theoretical investigation of the effect of the aerosol vertical distribution on the aerosol radiative effect (ARE). Four aerosol composition models (dust, polluted dust, pollution and pure scattering aerosols) with varying aerosol vertical profiles are incorporated into a radiative transfer model. The simulations show interesting spectral dependence of the ARE on the aerosol layer height. ARE increases with the aerosol layer height in the ultraviolet (UV: 0.25–0.42 μm) and thermal-infrared (TH-IR: 4.0–20.0 μm) regions, whereas it decreases in the visible-near infrared (VIS-NIR: 0.42–4.0 μm) region. Changes in the ARE with aerosol layer height are associated with different dominant processes for each spectral region. The combination of molecular (Rayleigh) scattering and aerosol absorption is the key process in the UV region, whereas aerosol (Mie) scattering and atmospheric gaseous absorption are key players in the VIS-NIR region. The longwave emission fluxes are controlled by the environmental temperature at the aerosol layer level. ARE shows maximum sensitivity to the aerosol layer height in the TH-IR region, followed by the UV and VIS-NIR regions. These changes are significant even in relatively low aerosol loading cases (aerosol optical depth ∼0.2–0.3). Dust aerosols are the most sensitive to altitude followed by polluted dust and pollution in all three different wavelength regions. Differences in the sensitivity of the aerosol type are explained by the relative strength of their spectral absorption/scattering properties. The role of surface reflectivity on the overall altitude dependency is shown to be important in the VIS-NIR and UV regions, whereas it is insensitive in the TH-IR region. Our results indicate that the vertical distribution of water vapor with respect to the aerosol layer is an important factor in the ARE estimations. Therefore, improved estimations of the water vapor profiles are needed for the further reduction in uncertainties associated with the ARE estimation.