The UK-China Collaboration to Optimise net zero Policy options for Air Quality and health (COP-AQ) is pleased to invite early career researchers to join the “Data Science for Climate and Air Quality Research” Winter School 2021. COP-AQ is led by Prof. Zongbo Shi at the University of Birmingham and is funded by the UK Research and Innovation – Natural Environment Research Council.

The main objectives are (1) To introduce the theoretical basis of the openair R package and machine learning; (2) To provide practical training by applying the theory to case studies; (3) To provide hands-on experience in using existing codes for climate, air quality, health and economics research.

The Winter School has six sessions, which are scheduled to be from 8:00 or 9:00 am UK time and 4:00 or 5:00 pm China time.

The first training session “Introduction to openair” is scheduled for the 13th of December at 8:00-10:00 am (UK time) which will be given by Prof. David Carslaw (University of York). This session aims to introduce openair and its applications in air quality research.

The second training session “Machine learning for intervention studies” is scheduled for the 15th of December at 8:00-10:00 am (UK time) which will be given by Dr Qili Dai (Nankai University) and Dr Congbo Song (University of Birmingham). This session aims to introduce recent advances in weather normalization of air pollutant concentrations and their application in policy evaluation.

The third training session “Emulation of air quality in China” is scheduled for the 6th of January 2022 at 8:00-11:00 am (UK time) which will be given by Dr Luke Conibear and Prof. Dom Spracklen (University of Leeds). This session aims to provide training in the methods of statistical emulation for air quality research. Objectives: (1) Learn how to apply emulators for simulation of air quality and health impacts; (2) Learn about the basics of emulator development.

The fourth training session “Data science for air pollution exposure science and personal monitoring” is scheduled for the 21st of February 2022 at 9:00-11:00 am (UK time) which will be given by Dr Yiqun Han and Dr Hanbin Zhang (Imperial College London), Lia Chatzidiakou (University of Cambridge). This session aims to introduce applications of personal sensors in air quality research. Main content: (1) To learn study designs and methods of using personal sensors in exposure science and environmental epidemiological studies; (2) To provide real-world measurement datasets for hands-on analysis; (3) To introduce recent advances in the research field.

The fifth training session “Machine learning for airborne particle identification from spectral / image datasets” is scheduled for the 24th of February 2022 at 9:00-11:00 am (UK time) which will be given by Prof. David Topping and Prof. Hugh Coe (University of Manchester). This session aims to introduce the diverse set of tools now used to try and resolve characteristics of particulate matter from instrument response functions. This workshop will introduce a broad family of methods used for classification of aerosol particles from spectral datasets. Given the sparsity in controlled laboratory studies versus ambient datasets, the focus will be mainly on unsupervised methods. The session will also focus on bio-aerosol datasets, as a demonstrator of potential application.

The sixth training session “Data science in GHG/air pollutant emission estimates” is scheduled for the 28th of February 2022 at 8:30-10:30 am (UK time) which will be given by Dr Jing Meng (University College London). This session will introduce the decomposition method, which is widely used to quantify the contribution of various socioeconomic factors to change in emissions. This workshop will allow attendees (1) To get an overview of the concepts and application of decomposition analysis (2) To perform a decomposition analysis with provincial carbon emissions in China (3) To be able to conduct a decomposition analysis to identify the driving forces of emission changes .