Surface air pollution affects ecosystems and people’s health. However, traditional models have low prediction accuracy. Therefore, a hybrid model for accurately predicting daily surface PM_2.5 concentrations was integrated with wavelet (W), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and bidirectional gated recurrent unit (BiGRU). The data for meteorological factors and air pollutants in Guangzhou City from 2014 to 2020 were utilized as inputs to the models. The W-CNN-BiGRU-BiLSTM hybrid model demonstrated strong performance during the predicting phase, achieving an R (correlation coefficient) of 0.9952, a root mean square error (RMSE) of 1.4935 μg/m³, a mean absolute error (MAE) of 1.2091 μg/m³, and a mean absolute percentage error (MAPE) of 7.3782%. Correspondingly, the accurate prediction of surface PM_2.5 concentrations is beneficial for air pollution control and urban planning.

Fine particulate matter (PM_2.5) affects climate change and human health. Therefore, the prediction of PM_2.5 level is particularly important for regulatory planning. The main objective of the study is to predict PM_2.5 concentration employing an artificial neural network (ANN). The annual change in PM_2.5 in Liaocheng from 2014 to 2021 shows a gradual decreasing trend. The air quality in Liaocheng during lockdown and after lockdown periods in 2020 was obviously improved compared with the same periods of 2019. The ANN employed in the study contains a hidden layer with 6 neurons, an input layer with 11 parameters, and an output layer. First, the ANN is used with 80% of data for training, then with 10% of data for verification. The value of correlation coefficient (R) for the training and validation data is 0.9472 and 0.9834, respectively. In the forecast period, it is demonstrated that the ANN model with Bayesian regularization (BR) algorithm (trainbr) obtained the best forecasting performance in terms of R (0.9570), mean absolute error (4.6 μg/m³), and root mean square error (6.6 μg/m³), respectively. The ANN model has produced accurate results. These results prove that the ANN is effective in monthly PM_2.5 concentration predicting due to the fact that it can identify nonlinear relationships between the input and output variables.

Black carbon (BC) aerosols emitted from natural and anthropogenic sources induce positive radiative forcing and global warming, which in turn significantly affect the Asian summer monsoon (ASM). However, many aspects of the BC effect on the ASM remain elusive and largely inconsistent among previous studies, which is strongly dependent on different low-level thermal feedbacks over the Asian continent and the surrounding ocean. This study examines the response of the ASM to BC forcing in comparison with the effect of doubled greenhouse gases (GHGs) by analyzing the Precipitation Driver Response Model Intercomparison Project (PDRMIP) simulations under an extremely high BC level (10 times modern global BC emissions or concentrations, labeled BC ×10) from nine global climate models (GCMs). The results show that although BC and GHGs both enhance the ASM precipitation minus evaporation (P−E; a 13.6 % increase for BC forcing and 12.1 % for GHGs from the nine-model ensemble, respectively), there exists a much larger uncertainty in changes in ASM P−E induced by BC than by GHGs. The summer P−E is increased by 7.7 % to 15.3 % due to these two forcings over three subregions, including East Asian, South Asian and western North Pacific monsoon regions. Further analysis of moisture budget reveals distinct mechanisms controlling the increases in ASM P−E induced by BC and GHGs. The change in ASM P−E by BC is dominated by the dynamic effect due to the enhanced large-scale monsoon circulation, whereas the GHG-induced change is dominated by the thermodynamic effect through increasing atmospheric water vapor. Radiative forcing of BC significantly increases the upper-level atmospheric temperature over the Asian region to enhance the upper-level meridional land–sea thermal gradient (MLOTG), resulting in a northward shift of the upper-level subtropical westerly jet and an enhancement of the low-level monsoon circulation, whereas radiative forcing of GHGs significantly increases the tropical upper-level temperature, which reduces the upper-level MLOTG and suppresses the low-level monsoonal circulation. Hence, our results indicate a different mechanism of BC climate effects under the extremely high BC level: that BC forcing significantly enhances the upper-level atmospheric temperature over the Asian region, determining ASM changes, instead of low-level thermal feedbacks as indicated by previous studies.

The relationship between initial Homo sapiens dispersal from Africa to East Asia and the orbitally paced evolution of the Asian summer monsoon (ASM)—currently the largest monsoon system—remains underexplored due to lack of coordinated synthesis of both Asian paleoanthropological and paleoclimatic data. Here, we investigate orbital-scale ASM dynamics during the last 280 thousand years (kyr) and their likely influences on early H. sapiens dispersal to East Asia, through a unique integration of i) new centennial-resolution ASM records from the Chinese Loess Plateau, ii) model-based East Asian hydroclimatic reconstructions, iii) paleoanthropological data compilations, and iv) global H. sapiens habitat suitability simulations. Our combined proxy- and model-based reconstructions suggest that ASM precipitation responded to a combination of Northern Hemisphere ice volume, greenhouse gas, and regional summer insolation forcing, with cooccurring primary orbital cycles of ~100-kyr, 41-kyr, and ~20-kyr. Between ~125 and 70 kyr ago, summer monsoon rains and temperatures increased in vast areas across Asia. This episode coincides with the earliest H. sapiens fossil occurrence at multiple localities in East Asia. Following the transcontinental increase in simulated habitat suitability, we suggest that ASM strengthening together with Southeast African climate deterioration may have promoted the initial H. sapiens dispersal from their African homeland to remote East Asia during the last interglacial.

Atmospheric absorptive aerosols exert complicated effects on the climate system, two of which are through their direct radiative forcing and snow-darkening forcing. Compared to black carbon, the snow-darkening effect of dust on climate has been scarcely explored till now. When depositing in snow, dust can reduce the albedo of snow by darkening it and increasing the snowmelt. In this study, the snow-darkening effect of dust, as well as the direct radiative effect, on the Indian summer monsoon are evaluated by atmospheric general circulation model experiments. The results show that the snow-darkening and direct radiative forcing of dust both have significant impacts on the onset of the Indian monsoon, but they are distinctly opposite. The snow-darkening effect of dust weakens the Indian monsoon precipitation during May and June, opposite to black carbon. The surface temperature over central Asia and the western Tibetan Plateau becomes warmer due to the dust-induced decrease in snow cover, which leads to a local low-level cyclonic anomaly as well as an anticyclonic anomaly over the Indian subcontinent and Arabian Sea. This circulation pattern allows air currents penetrating into the Indian subcontinent more from central Asia but less from the Indian Ocean. In contrast, the direct radiative forcing of dust warms the low troposphere over the Arabian Peninsula, which intensifies moisture convergence and precipitation over the Indian monsoon region. The upper tropospheric atmospheric circulation over Asia is also sensitive to both effects. Compared to previous studies which emphasized the temperature over the Tibetan Plateau, our results further highlight an important role of surface/low tropospheric temperature changes over dust source areas, which can also significantly modify the response of summer monsoon. Thus, links between the climatic impact of dust and complicated thermal conditions over Asia are of importance and need to be clarified accurately.

Precipitation on the NE Tibetan Plateau was driven by combined high- and low-latitude processes during the warm Late Oligocene.