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An international consortium promoting iDirac VOC developments

Lead Research Organisation: CRANFIELD UNIVERSITY
Department Name: Engineering and Applied Science

Abstract

Atmospheric measurements of isoprene and other volatile organic compounds (VOC) are limited in much of the world. This stems from having few instruments which are robust, can be run autonomously in real-world conditions, and are reasonably low cost. One such instrument is the iDirac, developed with NERC funding, which was designed to meet these criteria and to measure isoprene. In this project, we will establish an international network of research scientists who are committed to expanding the available longer-term observations of isoprene and monoterpenes in undersampled regions and to developing innovative lower-cost alternatives, flux and atmospheric reactivity measurements. Such developments would greatly enhance our understanding of the fluxes of biogenic species and of the oxidative removal of these and many other trace species from the atmosphere. The partners are Alex Guenther (UC Irvine, USA), Yuan Bin (Jinan University, PRC), and Kerneels Jaars (SAEON, RSA) who contribute a unique set of expertise and facilities. The group will work together on three main topics: (i) improving the instrument design for better performance and ease of construction and deployment; (ii) piloting new uses (extending the range of species measured, deploying in new, under-sampled locations, and integrating it into new systems novel applications); (iii) identifying new opportunities for development and deployment. This will be achieved through a mix of on-line collaboration, exchange of expertise and personnel, and use of measurement facilities. The ultimate objective is to kickstart an international group working collegiately on iDirac-related developments, loosely analogous to the grass-roots, collegiate approach used for the Model of Emissions of Gases and Aerosols from Nature (MEGAN).

Publications

10 25 50
 
Description New field measurements to identify missing chemical reactivity in the remote atmosphere
Amount £807,954 (GBP)
Funding ID UKRI1265 
Organisation Natural Environment Research Council 
Sector Public
Country United Kingdom
Start 07/2025 
End 07/2029
 
Description Collaboration on use of iDIrac 
Organisation Plymouth Marine Laboratory
Country United Kingdom 
Sector Academic/University 
PI Contribution We are helping the Plymouth Marine Laboratory to construct two iDirac instruments for use on their marine facilities.
Collaborator Contribution They are paying for the parts and providing the labour.
Impact not yet
Start Year 2024
 
Description Discussions on development and use of iDirac 
Organisation University of California, Irvine
Country United States 
Sector Academic/University 
PI Contribution We have provided expertise in the construction and operation of iDirac. This was achieved through remote access communication, a visit by Valerio Ferracci to UCI in June 2022 and a meeting with Prof Guenther and a colleague in London in Sept 2022.
Collaborator Contribution They have built their own version of iDirac for use with a relaxed eddy correlation approach to measure isoprene emissions in the Brazilian forests. We hope to bring the approach to our own work in the UK and Africa and to see if it can be extended for use with the DMS measurements.
Impact No impact yet.
Start Year 2021
 
Description Discussions on development and use of iDirac (2021 - Still Active) 
Organisation North-West University
Country South Africa 
Sector Academic/University 
PI Contribution Expertise in use and construction of iDirac
Collaborator Contribution Two visits to Cranfield, one for discussions and an extended one to gain experience and make progress in constructing an iDirac for their use. NWU have led a proposal to GCRF to develop this collaboration on understanding atmospheric change in Africa (outcome awaited).
Impact None yet
Start Year 2021
 
Title Optimizing the Temperature Sensitivity of the Isoprene Emission Model MEGAN in Different Ecosystems Using a Metropolis-Hastings Markov Chain Monte Carlo Method 
Description Software and Data Repository for MEGAN Optimization Experiments Using MHMCMC This repository is associated with the following manuscript, which has been submitted to JGR Biogeosciences:  Christian Alexander DiMaria, Dylan B. A. Jones, Valerio Ferracci, et al. Optimizing the Temperature Sensitivity of the Isoprene Emission Model MEGAN in Different Ecosystems Using a Metropolis-Hastings Markov Chain Monte Carlo Method. ESS Open Archive . February 05, 2025. DOI: 10.22541/essoar.173877770.06627485/v1. https://essopenarchive.org/doi/full/10.22541/essoar.173877770.06627485   The repository contains all the data and code required to repeat our MEGAN temperature response optimization experiments. In particular, starting from the raw isoprene concentration or flux time series data, users of this repo can: Filter the observations using ancillary meteorological data (also included in repo) Normalize the filtered observations to extract the temperature response function Perform a Levenberg-Marquardt optimization for any parameter combinations at all field sites Perform MHMCMC optimization for any parameter combination at all field sites Recreate the figures from the associated manuscript. The code is a mixture of Python scripts, MATLAB scripts, and Jupyter Notebooks. All code has been pre-run, and the outputs (including figures) are included in the repository.  Citation Policy and Stipulations For Use Please cite this repository and / or the accompanying manuscript if you use or modify the Python scripts, Matlab scripts, or Jupyter Notebooks, for your own research. You may also use anything contained in the /repo/output/ folder, the /repo/data/geos-chem/ folder, and the /repo/data/normalized_measurements/ and /repo/data/filtered_measurements/ folders, with appropriate reference to this repository and / or accompanying manuscript. IMPORTANT: Raw measurements are included for code validation purposes only. In particular, they are required in order to run the data filtering Jupyter Notebook. These data are not to be used for any other purposes without consulting and crediting their original authors (see reference list at the bottom of this document). If you use any of the datasets in /repo/data/raw_measurements/ in your own research, you MUST consult and credit the ORIGINAL authors. Citing this repository is not sufficient in those cases. ### INSTRUCTIONS ###  Do not modify the directory structure of this repository. All of the code has been pre-run, so the outputs needed to produce the figures from the manuscript are already present in the repo. If you wish to regenerate the main results on your own, you must run the code in the following order:   1. obs_filter_fig2_fig3_figS8-S17.ipynb (this filters the raw data)   2. normalize.py (this normalizes the filtered observations to extract the temperature response)   3. LM_optimization.py (this performs the LM optimization for all parameter configurations and all sites)   4. config_1d.m (K2 optimization using MCMC shown in Figure 6) and config_2o.m (CT1 and CT2 optimization using MCMC shown in Figure 7)   Additional scripts and notebooks are included to reproduce the figures from the manuscript; these are labelled accordingly. Figure S1 was produced by modifying fig5.py to use the normalized STM measurements, and Figure S5 was produced by running config_1d.m and config_1e.py with the observation error set to a constant value of 0.05, then running fig6.py with these output data for the field sites AABC, ALH, and ACM.   To run all MCMC configurations at all sites, a separate run_all.m script is included. The output of that script can be visualized using visualize_all_mcmc_results.py.   The MATLAB code has been tested with R2013b and R2024b on both Linux and MacOS. The Python code and Jupyter notebooks have been tested and validated with Python version 3.8 on MacOS, provided that the necessary libraries (imported at the top of each script or Jupyter notebook) are installed. If necessary, a working Python library can be created using Anaconda and the libraries listed in "python_environment.txt" with Python 3.8.12 (note that this environment contains many more libraries than are strictly required for running the code in this repository; it is included here for the sake of reproducibility only). 
Type Of Technology Software 
Year Produced 2025 
Open Source License? Yes  
URL https://zenodo.org/doi/10.5281/zenodo.15262184
 
Title Optimizing the Temperature Sensitivity of the Isoprene Emission Model MEGAN in Different Ecosystems Using a Metropolis-Hastings Markov Chain Monte Carlo Method 
Description Software and Data Repository for MEGAN Optimization Experiments Using MHMCMC This repository is associated with the following manuscript, which has been submitted to JGR Biogeosciences:  Christian Alexander DiMaria, Dylan B. A. Jones, Valerio Ferracci, et al. Optimizing the Temperature Sensitivity of the Isoprene Emission Model MEGAN in Different Ecosystems Using a Metropolis-Hastings Markov Chain Monte Carlo Method. ESS Open Archive . February 05, 2025. DOI: 10.22541/essoar.173877770.06627485/v1. https://essopenarchive.org/doi/full/10.22541/essoar.173877770.06627485   The repository contains all the data and code required to repeat our MEGAN temperature response optimization experiments. In particular, starting from the raw isoprene concentration or flux time series data, users of this repo can: Filter the observations using ancillary meteorological data (also included in repo) Normalize the filtered observations to extract the temperature response function Perform a Levenberg-Marquardt optimization for any parameter combinations at all field sites Perform MHMCMC optimization for any parameter combination at all field sites Recreate the figures from the associated manuscript. The code is a mixture of Python scripts, MATLAB scripts, and Jupyter Notebooks. All code has been pre-run, and the outputs (including figures) are included in the repository.  Citation Policy and Stipulations For Use Please cite this repository and / or the accompanying manuscript if you use or modify the Python scripts, Matlab scripts, or Jupyter Notebooks, for your own research. You may also use anything contained in the /repo/output/ folder, the /repo/data/geos-chem/ folder, and the /repo/data/normalized_measurements/ and /repo/data/filtered_measurements/ folders, with appropriate reference to this repository and / or accompanying manuscript. IMPORTANT: Raw measurements are included for code validation purposes only. In particular, they are required in order to run the data filtering Jupyter Notebook. These data are not to be used for any other purposes without consulting and crediting their original authors (see reference list at the bottom of this document). If you use any of the datasets in /repo/data/raw_measurements/ in your own research, you MUST consult and credit the ORIGINAL authors. Citing this repository is not sufficient in those cases. ### INSTRUCTIONS ###  Do not modify the directory structure of this repository. All of the code has been pre-run, so the outputs needed to produce the figures from the manuscript are already present in the repo. If you wish to regenerate the main results on your own, you must run the code in the following order:   1. obs_filter_fig2_fig3_figS8-S17.ipynb (this filters the raw data)   2. normalize.py (this normalizes the filtered observations to extract the temperature response)   3. LM_optimization.py (this performs the LM optimization for all parameter configurations and all sites)   4. config_1d.m (K2 optimization using MCMC shown in Figure 6) and config_2o.m (CT1 and CT2 optimization using MCMC shown in Figure 7)   Additional scripts and notebooks are included to reproduce the figures from the manuscript; these are labelled accordingly. Figure S1 was produced by modifying fig5.py to use the normalized STM measurements, and Figure S5 was produced by running config_1d.m and config_1e.py with the observation error set to a constant value of 0.05, then running fig6.py with these output data for the field sites AABC, ALH, and ACM.   To run all MCMC configurations at all sites, a separate run_all.m script is included. The output of that script can be visualized using visualize_all_mcmc_results.py.   The MATLAB code has been tested with R2013b and R2024b on both Linux and MacOS. The Python code and Jupyter notebooks have been tested and validated with Python version 3.8 on MacOS, provided that the necessary libraries (imported at the top of each script or Jupyter notebook) are installed. If necessary, a working Python library can be created using Anaconda and the libraries listed in "python_environment.txt" with Python 3.8.12 (note that this environment contains many more libraries than are strictly required for running the code in this repository; it is included here for the sake of reproducibility only). 
Type Of Technology Software 
Year Produced 2025 
Open Source License? Yes  
URL https://zenodo.org/doi/10.5281/zenodo.15262183