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Fox News anchor Bret Baier examines the U.S. power supply on'Special Report.' The global race to harness the power of artificial intelligence (AI) has begun. President Donald Trump got it right from the start when he issued an executive order in January to strengthen America's AI – the next great technological forefront. From Day One as Environmental Protection Agency (EPA) administrator, it was clear that EPA would have a major hand in permitting reform to cut down barriers that have acted as a roadblock so we can bolster the growth of AI and make America the AI capital of the world. In fact, it's an endeavor so important, it is a core pillar of my Powering the Great American Comeback initiative.

President Trump this week continued to make his environmental priorities clear by vowing to open up hundreds of coal power plants in the United States in an effort to advance competition against China. "After years of being held captive by Environmental Extremists, Lunatics, Radicals, and Thugs, allowing other Countries, in particular China, to gain tremendous Economic advantage over us by opening up hundreds of all Coal Fire Power Plants, I am authorizing my Administration to immediately begin producing Energy with BEAUTIFUL, CLEAN COAL," Trump wrote in a post on social media Monday. Though the post was not linked to any particular policy plans or documents, it arrives as the White House takes aim at various environmental agencies and clean-energy initiatives. In the last week alone, the administration has announced plans to significantly roll back regulations that govern coal production and to potentially lay off up to 65% of scientists and researchers at the Environmental Protection Agency, among other actions. Coal accounts for about 16% of the country's electricity generation, according to the U.S. Energy Information Administration -- down from about 50% in 2000 as natural gas and nuclear and renewable energy have grown.

This significant contribution makes it a critical sector for climate change mitigation, as reducing emissions from transportation is essential for achieving global climate goals. The sector's transformation through electrification, automation, and intelligent infrastructure offers promising avenues for substantial emissions reductions (Sciarretta et al., 2020; International Energy Agency, 2023; McKinsey Center for Future Mobility, 2023). However, the success of these innovations is critically dependent on the availability of suitable and accurate emission estimation models to guide the design and deployment of new technologies. Motor Vehicle Emission Simulation (MOVES) (U.S. Environmental Protection Agency, 2022), one of the most well-established emission estimation models, serves as the official and state-of-the-art emission estimation model in the U.S., provided, enforced, and maintained by the U.S. Environmental Protection Agency (EPA). Despite its technical certification, MOVES' processing and software is tailored for two specific governmental uses: State Implementation Plans and Conformity Analyses U.S. Environmental Protection Agency (2021), which are for states to achieve and maintain air quality standards; and its use beyond trained practitioners and these specific analyses poses two main limitations. First, a steep learning curve, computational demands, and complex inputs make it difficult for researchers and practitioners to use. In particular, MOVES has rigid input requirements, including a combination of toggle-based settings within its GUI and structured input files in specific formats. Second, MOVES is tailored for macroscopic analysis and is unsuitable for microscopic applications, such as control and optimization, which commonly require second-by-second emission calculations for individual actions and vehicles.

Sweeping calls for Americans in swathes of the country to alter their behavior to reduce air pollution were today slammed as'outrageous.' Indiana's environment department urged residents to turn off their lights to reduce unhealthy levels of ozone, while officials in Southern California are advising people drive slow this weekend to limit the amount of dust released into the air. Both recommendations appear to have been passed down by AirNow, a federal agency that issues guidelines for what to do in situations where air pollution is high. While these unusual advisories have only officially been instated in two states, Government data shows at least 25 states have similar air pollution levels. Ohio and other parts of the Midwest appear to be most at risk.

For years, the Environmental Protection Agency's regulation of radiation, carcinogens, and other toxic chemicals has been based on the cautious scientific reasoning that considers even slight exposure to toxins potentially risky to public health. From that premise, the EPA has assessed a wide range of pollution, including lung-clogging particulate matter, Superfund cleanup, water treatment, radiation exposure, and as well as risk assessments for carcinogens like benzene. That time-honored approach may be changing because of easy-to-overlook phrasing within a paragraph buried in the proposed "Strengthening Transparency In Regulatory Science Rule," a regulation that will bar the EPA from considering a wide range of scientific studies in its rule making. With a few sentences buried in the seven-page Federal Register text, the EPA is opening the door to a new scientific approach that--in a worst-case scenario--could further relax regulations because of the assumption that a little pollution is actually beneficial. Some scientists have considered the implications of this paragraph and described a whole array of potential problems to Mother Jones. Written in incredibly vague language, most scientists were unable to explain which pollutants or regulations were the prime target.

Fine particulate matter (PM$_{2.5}$) is one of the criteria air pollutants regulated by the Environmental Protection Agency in the United States. There is strong evidence that ambient exposure to (PM$_{2.5}$) increases risk of mortality and hospitalization. Large scale epidemiological studies on the health effects of PM$_{2.5}$ provide the necessary evidence base for lowering the safety standards and inform regulatory policy. However, ambient monitors of PM$_{2.5}$ (as well as monitors for other pollutants) are sparsely located across the U.S., and therefore studies based only on the levels of PM$_{2.5}$ measured from the monitors would inevitably exclude large amounts of the population. One approach to resolving this issue has been developing models to predict local PM$_{2.5}$, NO$_2$, and ozone based on satellite, meteorological, and land use data. This process typically relies developing a prediction model that relies on large amounts of input data and is highly computationally intensive to predict levels of air pollution in unmonitored areas. We have developed a flexible R package that allows for environmental health researchers to design and train spatio-temporal models capable of predicting multiple pollutants, including PM$_{2.5}$. We utilize H2O, an open source big data platform, to achieve both performance and scalability when used in conjunction with cloud or cluster computing systems.