Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties

Abstract

Abstract. Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution which is challenging to map on a global scale. Here, we present a data-driven ozone-mapping workflow generating a transparent and reliable product. We map the global distribution of tropospheric ozone from sparse, irregularly placed measurement stations to a high-resolution regular grid using machine learning methods. The produced map contains the average tropospheric ozone concentration of the years 2010–2014 with a resolution of 0.1∘ × 0.1∘. The machine learning model is trained on AQ-Bench (“air quality benchmark dataset”), a pre-compiled benchmark dataset consisting of multi-year ground-based ozone measurements combined with an abundance of high-resolution geospatial data. Going beyond standard mapping methods, this work focuses on two key aspects to increase the integrity of the produced map. Using explainable machine learning methods, we ensure that the trained machine learning model is consistent with commonly accepted knowledge about tropospheric ozone. To assess the impact of data and model uncertainties on our ozone map, we show that the machine learning model is robust against typical fluctuations in ozone values and geospatial data. By inspecting the input features, we ensure that the model is only applied in regions where it is reliable. We provide a rationale for the tools we use to conduct a thorough global analysis. The methods presented here can thus be easily transferred to other mapping applications to ensure the transparency and reliability of the maps produced.

Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset

“…We chose machine learning as an alternative method to propose new station locations, which is a task that is also tackled by using an atmospheric chemistry model [42]. Although we show that the number of underrepresented test samples is not a significant issue for the prediction on the test dataset, underrepresented locations become problematic in the case of applying the models to areas outside the AQ-Bench dataset, e.g., in (global) mapping studies [13,41,43].…”

“…Ref. [13] showed that forward feature selection applied on AQ-Bench leads to 31 features. The data split is kept as in AQ-Bench with 60% training (approximately 3300 samples) and 20% validation and test samples (roughly 1110 samples, respectively).…”

“…What is rarely done, to our knowledge, is to explain the differences between various machine learning architectures applied to the same task. [13].…”

Correcting ozone biases in a global chemistry–climate model: implications for future ozone

“…In the absence of a global observation-based ozone climatology, we apply the CAMS reanalysis product in our analysis. We note that recent studies have explored the fusion of observations and model output to generate surface O 3 products at a global scale (Chang et al, 2019;Betancourt et al, 2022), but these approaches only work well in regions where measurement sites are available. The CAMS reanalysis provides surface concentrations at a scale comparable with our model, and thus avoids uncertainties associated with the spatial representativeness of observations when using measured concentrations.…”

Ways forward for Machine Learning to make useful global environmental datasets from legacy observations and measurements

“…If training datasets don’t themselves contain error uncertainty values, scientists could experiment with Bayesian-style priors that they themselves estimate so that uncertainty can still be properly represented and propagated (e.g., ref. 5 ).…”