Global PM Prediction and Associated Mortality to 2100 under Different Climate Change Scenarios.

Ambient fine particulate matter (PM) has severe adverse health impacts, making it crucial to reduce PM exposure for public health. Meteorological and emissions factors, which considerably affect the PM concentrations in the atmosphere, vary substantially under different climate change scenarios. In this work, global PM concentrations from 2021 to 2100 were generated by combining the deep learning technique, reanalysis data, emission data, and bias-corrected CMIP6 future climate scenario data.

Development of an integrated machine-learning and data assimilation framework for NO emission inversion.

As major air pollutants, nitrogen oxides (NO, mainly comprising NO and NO) not only have adverse effects on human health but also contribute to the formation of secondary pollutants, such as ozone and particulate nitrate. To acquire reasonable NO simulation results for further analysis, a reasonable emission inventory is needed for three-dimensional chemical transport models (3D-CTMs). In this study, a comprehensive emission adjustment framework for NO emission, which integrates the simulation results of the 3D-CTM, surface NO measurements, the three-dimensional variational data assimilation method, and an ensemble back propagation neural network, was proposed and applied to correct NO emissions over China for the summers of 2015 and 2020.

Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong.

Seventy-percent of the terrestrial area of Hong Kong is covered by vegetation and 40% is protected as the Country Park. The above-ground biomass (AGB) acts as reliable source of carbon sink and while Hong Kong has recognized the importance of carbon sink in forest and urged for forest protection in the latest strategic plan, yet no study has been conducted on assessing the baseline of terrestrial AGB and its carbon storage. This study compared and estimated the AGB by the traditional allometric modeling and the Light Detection and Ranging (LiDAR) plot metrics at plot-level in a subtropical forest of Hong Kong.

Fractal analysis of recurrence networks constructed from the two-dimensional fractional Brownian motions.

In this study, we focus on the fractal property of recurrence networks constructed from the two-dimensional fractional Brownian motion (2D fBm), i.e., the inter-system recurrence network, the joint recurrence network, the cross-joint recurrence network, and the multidimensional recurrence network, which are the variants of classic recurrence networks extended for multiple time series.

A novel method for identifying hotspots and forecasting air quality through an adaptive utilization of spatio-temporal information of multiple factors.

Air pollution exerts serious impacts on human health and sustainable development. The accurate forecasting of air quality can guide the formulation of mitigation strategies and reduce exposure to air pollution. It is beneficial to explicitly consider both spatial and temporal information of multiple factors, e.

Nonlinear dimensionality reduction of data lying on the multicluster manifold.

A new method, which is called decomposition-composition (D-C) method, is proposed for the nonlinear dimensionality reduction (NLDR) of data lying on the multicluster manifold. The main idea is first to decompose a given data set into clusters and independently calculate the low-dimensional embeddings of each cluster by the decomposition procedure. Based on the intercluster connections, the embeddings of all clusters are then composed into their proper positions and orientations by the composition procedure.