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Surface PM2.5 concentrations and aerosol optical depth (AOD) are two air pollution metrics tightly connected. Many studies have used AOD to derive PM2.5 concentrations without investigating their inconsistencies. Here, we explored the associations between surface PM2.5 and AOD using ground-level data from 19 stations in China during 2017–2019. Unexpectedly, we found low correlation coefficients of 0.03–0.60 between daily PM2.5 and AOD for most sites. Such decoupling between PM2.5 and AOD is further compared to simultaneous meteorological factors such as air temperature, specific humidity, sea level pressure, and wind speed. We found that specific humidity dominates the correlations with normalized PM2.5-AOD differences at 14 out of 19 sites. On average, specific humidity increases from 2.83 g kg−1 for the cases with low AOD but high PM2.5–11.89 g kg−1 for those with high AOD but low PM2.5, indicating that hygroscopic growth of aerosols may play an important role in decoupling the associations between PM2.5 and AOD. Random forest (RF) models using AOD as the only input yield a low R of 0.49 between the predicted and observed PM2.5 concentrations. The inclusion of specific humidity in the RF model increases the R to 0.74, close to the R of 0.81 with three additional meteorological factors. Our study revealed a strong decoupling between PM2.5 and AOD and suggested including specific humidity as a key parameter in the retrieval of long-term PM2.5 using AOD data in China.

To mitigate global warming and the resulting climate risk, many countries have accelerated the optimization of industrial structures and mixture of energy type in an attempt to achieve carbon neutrality by the second half of the 21st century. Here, we present the first assessment of the quantitative benefits of population exposure to extreme heat (defined by the heat index) during 2040–2049 under two scenarios of global carbon neutrality by 2060 and 2050, i.e. moderate green (MODGREEN) and strong green (STRGREEN) recovery scenarios, relative to the baseline scenario of Shared Socioeconomic Pathway (SSP) 2–4.5. Global mean extreme heat days increase by 12.1 d yr−1 (108%) during 2040–2049 under the SSP2-4.5 scenario relative to the historical period (1995–2014). The aggravating extreme heat events could be mitigated by as much as 12% and 18% during 2040–2049 under the MODGREEN and STRGREEN scenarios, respectively. Following the changes in extreme heat days, global population exposure to extreme heat is mitigated by 27.3 billion person-days (7%) in the MODGREEN scenario and 39.9 billion person-days (11%) in the STRGREEN scenario during 2040–2049 relative to the SSP2-4.5 scenario. Such benefits from these low-carbon policies are larger in regional hotspots, including India and Northern Africa, which have experienced high population growth and have extremely limited medical infrastructure. Moreover, an early carbon neutrality (2050 vs 2060) could avoid 12.6 billion person-days exposure to extreme heat during 2040–2049. Our results provide an important scientific support for governments to drive early policymaking for climate change mitigation.

The land ecosystems of China are estimated to provide an important sink for the increased atmospheric carbon dioxide (CO2), but are undermined by severe ozone (O3) pollution. Mitigation of O3 damage to ecosystems remains a challenge considering that O3 precursors are emitted from a wide range of anthropogenic sectors and O3 formations are also affected by regional transport. Here, we combine chemical transport and dynamic vegetation models to quantify the benefits of sectoral and regional emission controls for the recovery of gross primary productivity (GPP) in the Yangtze River Delta (YRD). For sectoral emission controls, the largest mitigation of O3 damage to GPP in YRD by 3.1 ± 0.4 and 2.2 ± 0.2 Gg[C] d−1 with 50% reductions in the emissions from industry and transportation sectors, respectively. For regional emission controls, reducing 50% anthropogenic emissions outside YRD can mitigate GPP losses by 18.6 ± 3.5 Gg[C] d−1, larger than the recovery of 10.1 ± 1.6 Gg[C] d−1 by the 50% reductions of anthropogenic emissions within YRD. Moreover, summer months, especially July are the best period for GPP recovery from anthropogenic emission controls. Our results highlight the importance of sectoral and regional emission controls to mitigate O3 damage to ecosystem productivities in YRD.

Isoprene is a key biogenic volatile organic compound of vital importance for global climate change and air quality. Previous studies projecting future changes of isoprene emissions showed a wide range of uncertainties due to the discrepancies in emission schemes, climate models, and future scenarios. Here, we use an ensemble of models from the Coupled Model Intercomparison Project version 6 (CMIP6) to explore the spatiotemporal variations of global isoprene emissions at present day and their changes by the end of 21st century. At present day, most models predict similar emission rates of 400 Tg C yr−1 but with large differences in the long-term trends. For models using the scheme of Model of Emissions of Gases and Aerosols from Nature, isoprene emissions show limited changes during historical period but significant enhancements after the year 2000. However, for models using another scheme with strong CO2 inhibition effects, isoprene emissions show decreasing trends during historical period and moderate increasing trends after the year 2000. By the end of 21st century, the ensemble projection shows increases of 21–57% in isoprene emissions with the largest enhancement for the strongest warming scenario. Attribution shows that temperature is the dominant driver for the increase of isoprene emissions, no matter whether the CO2 inhibition effects are considered or not. The enhanced isoprene emissions increase the risks of ozone pollution in a warmer climate.

Diffuse radiation can promote ecosystem gross primary productivity (GPP) and strengthen land carbon sink. However, measurements of diffuse radiation at carbon flux sites are rare, limiting the explorations of diffuse fertilization efficiency (DFE), the percentage changes of GPP per unit diffuse photosynthetically active radiation (PAR). Here, we derive diffuse radiative fraction (Kd) at >200 FLUXNET sites based on an artificial neural network (ANN) and estimate DFE at individual sites around the world. Evaluations show that the ANN on average increases modeling-to-observation correlation coefficients and the Willmott index of Kd by 8% and reduces root-mean-square error by 14.7% compared to 11 empirical models. High Kd of 0.6–0.82 is located at mid-high latitudes and relatively low Kd of 0.46–0.58 is found at subtropical regions. The optimal Kd leading to maximum GPP is estimated to be 0.46 averaged for all FLUXNET sites. In response to 1 W m−2 enhancement of diffuse PAR, daytime GPP increases by 0.44–1.01% for tree species and 0.16–0.94% for non-tree species. Such GPP enhancement by diffuse radiation is on average 2.5 times of that by the same amount of direct radiation.