An early warning system for joint heat and ozone extremes in China

High temperatures exacerbate ground-level ozone production, resulting in a deadly combination of extreme heat and poor air quality that is especially dangerous for children, seniors, and people suffering from preexisting respiratory illnesses.

Like most of the globe, China is dealing with increasing temperatures and longer and more frequent heat waves. But, because of its rapid, energy-intensive development, it's also seeing increased production of the main precursors of ozone, volatile organic compounds (VOCs) and oxides of nitrogen (NOx). In a country as populous as China, this combination poses a serious threat to human health, especially in large urban areas such as Beijing.

Now, a team of collaborating researchers from the Harvard-China Project at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and Hong Kong Baptist University has identified large-scale climate patterns that could be used to predict the co-occurrence of extreme heat and ozone days in China months before they occur. Like predictions for hurricane and wildfire seasons, the forecasts could help the government prepare resources and implement policies to mitigate the severity of the season.

The research was published recently in the Proceedings of the National Academy of Sciences.

"We've already seen record-breaking heat waves around the globe this summer, including in China, where local emissions have led to substantial ozone pollution," said Fan Wang, a visiting fellow at SEAS and the Harvard-China Project, Ph.D. candidate at Hong Kong Baptist University, and co-lead author of the study. "Our research could have important implications in the future that would allow agencies such as the Ministry of Ecology and Environment in China to prepare for high summer heat and ozone in springtime."

The research team, led by Michael McElroy, the Gilbert Butler Professor of Environmental Studies at SEAS and the faculty chair of the Harvard-China Project, and Meng Gao, professor at Hong Kong Baptist University and former postdoctoral researcher at SEAS, looked to past meteorological data and daily ozone levels to spot patterns that could be used to predict the season.

Because of the lack of long-term daily observations of ground-level ozone concentrations, the researchers used a sophisticated machine learning model to reconstruct levels back to 2005. Using this dataset, the team identified patterns in sea surface warming in the western Pacific Ocean, the western Indian Ocean and the Ross Sea, off the coast of Antarctica, that preceded summers with high heat and ozone in northeast China, including Beijing.

Warm sea surface temperatures in these regions lead to a decrease in precipitation, cloud cover and circulation across this region of China, known as the North China Plain, which is home to roughly 300 million people.

"These sea surface temperature anomalies influence precipitation, radiation and more, which modulate the co-occurrence of heat waves and ozone pollution," said Gao, co-first author of the paper and Associate of the Harvard-China Project.

The team's model correlated these anomalies with increases in heat waves and ozone about 80 percent of the time.

Governmental agencies could use these predictions to not only issue warnings for human health and agriculture but to reduce the components of ozone and its precursors in the atmosphere before the extreme heat waves hit.

"The ability to forecast prospects for unusually hot summers and unusually high levels of summertime ozone in China simply on the basis of patterns of temperature observed months earlier in remote regions of the ocean is truly exciting," said McElroy.

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Neural network model helps predict site-specific impacts of earthquakes

In disaster mitigation planning for future large earthquakes, seismic ground motion predictions are a crucial part of early warning systems and seismic hazard mapping. The way the ground moves depends on how the soil layers amplify the seismic waves (described in a mathematical site "amplification factor"). However, geophysical explorations to understand soil conditions are costly, limiting characterization of site amplification factors to date.

A new study by researchers from Hiroshima University published on April 5 in the Bulletin of the Seismological Society of America introduced a novel artificial intelligence (AI)-based technique for estimating site amplification factors from data on ambient vibrations or microtremors of the ground.

Subsurface soil conditions, which determine how earthquakes affect a site, vary substantially. Softer soils, for example, tend to amplify ground motion from an earthquake, while hard substrates may dampen it. Ambient vibrations of the ground or microtremors that occur all over the Earth's surface caused by human or atmospheric disturbances can be used to investigate soil conditions. Measuring microtremors provides valuable information about the amplification factor (AF) of a site, thus its vulnerability to damage from earthquakes due to its response to tremors.

The recent study from Hiroshima University researchers introduced a new way to estimate site effects from microtremor data. "The proposed method would contribute to more accurate and more detailed seismic ground motion predictions for future earthquakes," says lead author and associate professor Hiroyuki Miura in the Graduate School of Advanced Science and Engineering. The study investigated the relationship between microtremor data and site amplification factors using a deep neural network with the goal of developing a model that could be applied at any site worldwide.

The researchers looked into a common method known as Horizontal-to-vertical spectral ratios (MHVR) which is usually used to estimate the resonant frequency of the seismic ground. It can be generated from microtremor data; ambient seismic vibrations are analyzed in three dimensions to figure out the resonant frequency of sediment layers on top of bedrock as they vibrate. Previous research has shown, however, that MHVR cannot reliably be used directly as the site amplification factor. So, this study proposed a deep neural network model for estimating site amplification factors from the MHVR data.

The study used 2012-2020 microtremor data from 105 sites in the Chugoku district of western Japan. The sites are part of Japan's national seismograph network that contains about 1700 observation stations distributed in a uniform grid at 20 km intervals across Japan. Using a generalized spectral inversion technique, which separates out the parameters of source, propagation, and site, the researchers analyzed site-specific amplifications.

Data from each site were divided into a training set, a validation set, and a test set. The training set were used to teach a deep neural network. The validation set were used in the network's iterative optimization of a model to describe the relationship between the microtremor MHVRs and the site amplification factors. The test data were a completely unknown set used to evaluate the performance of the model.

The model performed well on the test data, demonstrating its potential as a predictive tool for characterizing site amplification factors from microtremor data. However, notes Miura, "the number of training samples analyzed in this study (80) sites is still limited," and should be expanded before assuming that the neural network model applies nationwide or globally. The researchers hope to further optimize the model with a larger dataset.

Rapid and cost-effective techniques are needed for more accurate seismic ground motion prediction since the relationship is not always linear. Explains Miura, "By applying the proposed method, site amplification factors can be automatically and accurately estimated from microtremor data observed at arbitrary site." Going forward, the study authors aim to continue to refine advanced AI techniques to evaluate the nonlinear responses of the ground to earthquakes.

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