Air pollution poses a significant threat to public health and well-being,
particularly in urban areas. This study introduces a series of machine-learning
models that integrate data from the Sentinel-5P satellite, meteorological
conditions, and topological characteristics to forecast future levels of five
major pollutants. The investigation delineates the process of data collection,
detailing the combination of diverse data sources utilized in the study.
Through experiments conducted in the Milan metropolitan area, the models
demonstrate their efficacy in predicting pollutant levels for the forthcoming
day, achieving a percentage error of around 30%. The proposed models are
advantageous as they are independent of monitoring stations, facilitating their
use in areas without existing infrastructure. Additionally, we have released
the collected dataset to the public, aiming to stimulate further research in
this field. This research contributes to advancing our understanding of urban
air quality dynamics and emphasizes the importance of amalgamating satellite,
meteorological, and topographical data to develop robust pollution forecasting
models.

利用 Sentinel-5P 卫星、气象条件和拓扑特征的数据，本研究引入了一系列机器学习模型来预测未来 5 种主要污染物的水平。通过在米兰都市区进行的实验，这些模型在预测未来一天的污染物水平方面表现出了良好的效果，误差率约为 30%。该研究对提升我们对城市空气质量动态的理解以及强调结合卫星、气象和地形数据来开发强大的污染物预测模型的重要性做出了贡献。

城市空气污染预测：利用卫星观测和气象预报的机器学习方法

Urban Air Pollution Forecasting: a Machine Learning Approach leveraging  Satellite Observations and Meteorological Forecasts

This study investigates lightning at tall objects and evaluates the risk of
upward lightning (UL) over the eastern Alps and its surrounding areas. While
uncommon, UL poses a threat, especially to wind turbines, as the long-duration
current of UL can cause significant damage. Current risk assessment methods
overlook the impact of meteorological conditions, potentially underestimating
UL risks. Therefore, this study employs random forests, a machine learning
technique, to analyze the relationship between UL measured at Gaisberg Tower
(Austria) and $35$ larger-scale meteorological variables. Of these, the
larger-scale upward velocity, wind speed and direction at 10 meters and cloud
physics variables contribute most information. The random forests predict the
risk of UL across the study area at a 1 km$^2$ resolution. Strong near-surface
winds combined with upward deflection by elevated terrain increase UL risk. The
diurnal cycle of the UL risk as well as high-risk areas shift seasonally. They
are concentrated north/northeast of the Alps in winter due to prevailing
northerly winds, and expanding southward, impacting northern Italy in the
transitional and summer months. The model performs best in winter, with the
highest predicted UL risk coinciding with observed peaks in measured lightning
at tall objects. The highest concentration is north of the Alps, where most
wind turbines are located, leading to an increase in overall lightning
activity. Comprehensive meteorological information is essential for UL risk
assessment, as lightning densities are a poor indicator of lightning at tall
objects.

通过应用随机森林机器学习算法分析上升闪电的测量数据与 35 个大尺度气象变量之间的关系，这项研究评估了东阿尔卑斯山及其周边地区上升闪电的风险，发现大尺度上行速度、风速和方向以及云物理变量对上升闪电风险评估贡献最大，并预测了研究区域每平方公里的上升闪电风险。

利用气象再分析数据对高物体上行闪电进行时空风险评估

Spatio-seasonal risk assessment of upward lightning at tall objects  using meteorological reanalysis data

Cirrus clouds are key modulators of Earth's climate. Their dependencies on
meteorological and aerosol conditions are among the largest uncertainties in
global climate models. This work uses three years of satellite and reanalysis
data to study the link between cirrus drivers and cloud properties. We use a
gradient-boosted machine learning model and a Long Short-Term Memory (LSTM)
network with an attention layer to predict the ice water content and ice
crystal number concentration. The models show that meteorological and aerosol
conditions can predict cirrus properties with $R^2 = 0.49$. Feature
attributions are calculated with SHapley Additive exPlanations (SHAP) to
quantify the link between meteorological and aerosol conditions and cirrus
properties. For instance, the minimum concentration of supermicron-sized dust
particles required to cause a decrease in ice crystal number concentration
predictions is $2 \times 10^{-4}$ mg m\textsuperscript{-3}. The last 15 hours
before the observation predict all cirrus properties.

使用机器学习和气象和气溶胶条件，预测和分析云的特性和与之相关的因素，模型表明，气象和气溶胶条件可以以预测云的性质并具有显着的影响。