The MethaneSAT satellite instrument and its aircraft precursor, MethaneAIR, are imaging spectrometers designed to measure methane concentrations with wide spatial coverage, fine spatial resolution, and high precision compared to currently deployed remote sensing instruments. At 12 960 m cruise altitude above ground (13 850 m above sea level), MethaneAIR datasets have a 4.5 km swath gridded to 10 m × 10 m pixels with 17–20 ppb standard deviation on a flat scene. MethaneAIR was deployed in the summer of 2021 in the Permian Basin to test the accuracy of the retrieved methane concentrations and emission rates using the algorithms developed for MethaneSAT. We report here point source emissions obtained during a single-blind volume-controlled release experiment, using two methods. (1) The modified integrated mass enhancement (mIME) method estimates emission rates using the total mass enhancement of methane in an observed plume combined with winds obtained from Weather Research Forecast driven by High-Resolution Rapid Refresh meteorological data in Large Eddy Simulations mode (WRF-LES-HRRR). WRF-LES-HRRR simulates winds in stochastic eddy-scale (100–1000 m) variability, which is particularly important for low-wind conditions and informing the error budget. The mIME can estimate emission rates of plumes of any size that are detectable by MethaneAIR. (2) The divergence integral (DI) method applies Gauss's theorem to estimate the flux divergence fields through a series of closed surfaces enclosing the sources. The set of boxes grows from the upwind side of the plume through the core of each plume and downwind. No selection of inflow concentration, as used in the mIME, is required. The DI approach can efficiently determine fluxes from large sources and clusters of sources but cannot resolve small point emissions. These methods account for the effects of eddy-scale variation in different ways: the DI averages across many eddies, whereas the mIME re-samples many eddies from the LES simulation. The DI directly uses HRRR winds, while mIME uses WRF-LES-HRRR wind products. Emissions estimates from both the mIME and DI methods agreed closely with the single-blind volume-controlled experiments (N = 21). The York regression between the estimated emissions and the released emissions has a slope of 0.96 [0.84, 1.08], R = 0.83 and N = 21, with 30 % mean percentage error for the whole dataset, which indicates that MethaneAIR can quantify point sources emitting more than 200 kg h⁻¹ for the mIME and 500 kg h⁻¹ for the DI method. The two methods also agreed on methane emission estimates from various uncontrolled sources in the Permian Basin. The experiment thus demonstrates the powerful potential of the MethaneAIR instrument and suggests that the quantification method should be transferable to MethaneSAT if it meets the design specifications.

Assessing exposure to fine particulate matter (PM_2.5) across disadvantaged communities is understudied, and the air monitoring network is inadequate. We leveraged emerging low-cost sensors (PurpleAir) and engaged community residents to develop a community-based monitoring program across disadvantaged communities (high proportions of low-income and minority populations) in Southern California. We recruited 22 households from 8 communities to measure residential outdoor PM_2.5 concentrations from June 2021 to December 2021. We identified the spatial and temporal patterns of PM_2.5 measurements as well as the relationship between the total PM_2.5 measurements and diesel PM emissions. We found that communities with a higher percentage of Hispanic and African American population and higher rates of unemployment, poverty, and housing burden were exposed to higher PM_2.5 concentrations. The average PM_2.5 concentrations in winter (25.8 µg/m³) were much higher compared with the summer concentrations (12.4 µg/m³). We also identified valuable hour-of-day and day-of-week patterns among disadvantaged communities. Our results suggest that the built environment can be targeted to reduce the exposure disparity. Integrating low-cost sensors into a citizen-science-based air monitoring program has promising applications to resolve monitoring disparity and capture “hotspots” to inform emission control and urban planning policies, thus improving exposure assessment and promoting environmental justice.

Reducing methane (CH₄) emissions from the oil and gas (O&G) sector is crucial for mitigating climate change in the near term. MethaneSAT is an upcoming satellite mission designed to monitor basin-wide O&G emissions globally, providing estimates of emission rates and helping identify the underlying processes leading to methane release in the atmosphere. MethaneSAT data will support advocacy and policy efforts by helping to track methane reduction commitments and targets set by countries and industries. Here, we introduce a CH₄ retrieval algorithm for MethaneSAT based on the CO₂ proxy method. We apply the algorithm to observations from the maiden campaign of MethaneAIR, an airborne precursor to the satellite that has similar instrument specifications. The campaign was conducted during winter 2019 and summer 2021 over three major US oil and gas basins. Analysis of MethaneAIR data shows that measurement precision is typically better than 2 % at a 20×20 m² pixel resolution, exhibiting no strong dependence on geophysical variables, e.g., surface reflectance. We show that detector focus drifts over the course of each flight, likely due to thermal gradients that develop across the optical bench. The impacts of this drift on retrieved CH₄ can mostly be mitigated by including a parameter that squeezes the laboratory-derived, tabulated instrument spectral response function (ISRF) in the spectral fit. Validation against coincident EM27/SUN retrievals shows that MethaneAIR values are generally within 1 % of the retrievals. MethaneAIR retrievals were also intercompared with retrievals from the TROPOspheric Monitoring Instrument (TROPOMI). We estimate that the mean bias between the instruments is 2.5 ppb, and the latitudinal gradients for the two data sets are in good agreement. We evaluate the accuracy of MethaneAIR estimates of point-source emissions using observations recorded over the Permian Basin, an O&G basin, based on the integrated-mass-enhancement approach coupled with a plume-masking algorithm that uses total variational denoising. We estimate that the median point-source detection threshold is 100–150 kg h⁻¹ at the aircraft's nominal above-surface observation altitude of 12 km. This estimate is based on an ensemble of Weather Research and Forecasting (WRF) large-eddy simulations used to mimic the campaign's conditions, with the threshold for quantification set at approximately twice the detection threshold. Retrievals from repeated basin surveys indicate the presence of both persistent and intermittent sources, and we highlight an example from each case. For the persistent source, we infer emissions from a large O&G processing facility and estimate a leak rate between 1.6 % and 2.1 %, higher than any previously reported emission levels from a facility of its size. We also identify a ruptured pipeline that could increase total basin emissions by 2 % if left unrepaired; this pipeline was discovered 2 weeks before it was found by its operator, highlighting the importance of regular monitoring by future satellite missions. The results showcase MethaneAIR's capability to make highly accurate, precise measurements of methane dry-air mole fractions in the atmosphere, with a fine spatial resolution (∼ 20×20 m²) mapped over large swaths (∼ 100×100 km²) in a single flight. The results provide confidence that MethaneSAT can make such measurements at unprecedentedly fine scales from space (∼ 130×400 m² pixel size over a target area measuring ∼ 200×200 km²), thereby delivering quantitative data on basin-wide methane emissions.

Almost USD 3 billion per year is appropriated for wildfire management on public land in the United States. Recent studies have suggested that ongoing climate change will lead to warmer and drier conditions in the western United States, with a consequent increase in the number and size of wildfires, yet large uncertainty exists in these projections. To assess the influence of future changes in climate and land cover on lightning-caused wildfires in the national forests and parks of the western United States and the consequences of these fires on air quality, we link a dynamic vegetation model that includes a process-based representation of fire (LPJ-LMfire) to a global chemical transport model (GEOS-Chem). Under a scenario of moderate future climate change (RCP4.5), increasing lightning-caused wildfire enhances the burden of smoke fine particulate matter (PM), with mass concentration increases of ∼53 % by the late 21st century during the fire season in the national forests and parks of the western United States. In a high-emissions scenario (RCP8.5), smoke PM concentrations double by 2100. RCP8.5 also shows enhanced lightning-caused fire activity, especially over forests in the northern states.

Updated preindustrial fire emissions consistent with ice core records reduce the magnitude of estimated aerosol radiative forcing.

This review summarizes studies on the relationships between climate change and Valley Fever (VF), also termed Coccidioidomycosis, a potentially fatal upper-respiratory fungal infection caused by the pathogenic fungi, C. immitis or C. posadasii. The intensified onset of climate change has caused frequencies and possibly intensities of natural hazard events like dust storms and drought to increase, which has been correlated with greater prevalence of VF. These events, followed by changes in patterns of precipitation, not only pick up dust and spread it throughout the air, but also boost the growth and spread of Coccidioides. In California alone, cases of VF have increased fivefold from 2001 to 2021, and are expected to continue to increase. From 1999 to 2019, there was an average of 200 deaths per year caused by VF in the United States. The number of deaths caused by VF fluctuates year to year, but because more infections are predicted to occur due to a changing climate, deaths are expected to rise; thus, the rising prevalence of the disease is becoming a larger focus of the scientific community and poses an increased threat to public health. By reviewing recent and past studies on Coccidioidomycosis and its relationships with climate factors, we categorize future impacts of this disease on the United States, and highlight areas that need more study. Factors affecting the incidence of VF, such as modes of dispersal and the optimum environment for Coccidioides growth, that could potentially increase its prevalence as weather patterns change are discussed and how the endemic regions could be affected are assessed. In general, regions of the United States, including California and Arizona, where VF is endemic, are expanding and incidences of VF are increasing in those areas. The surrounding southern states, including Nevada, New Mexico, Utah, and Texas, are experiencing similar changes. In addition, the entire endemic region of the United States is predicted to spread northward as drought is prolonged and temperatures steadily increase. The findings from the keyword search from eight databases indicate that more studies on VF and its relation to dust and climate are needed especially for endemic states like Nevada that are currently not adequately studied. Overall, results of this survey summarize mechanisms and climate factors that might drive spread of VF and describes trends of incidence of VF in endemic states and predicted likely trends that might occur under a changing climate. Through reviewing recent and past studies of Coccidioidomycosis and its relationships with climate factors, future impacts of this disease have been categorized and speculated on effects it might have on the United States. Better understanding of how climate factors affect VF as well as identifying regions that require more research could inform both environmental managers and medical professionals with the resources needed to make more accurate predictions, design better mitigation strategies, send timely warnings, and protect public health. Shortened version This review explores how climate change affects Valley Fever (VF), a dangerous fungal infection caused by C. immitis or C. posadasii. Climate change has increased natural hazard events such as dust storms and droughts, which have caused the spread of VF. Cases of the disease have increased fivefold between 2001 and 2021 in California alone, and it poses an increasing threat to public health. The review summarizes mechanisms that drive the spread of VF and highlights trends in endemic states under a changing climate. It recommends more studies on VF and its relation to dust and climate, especially for states like Nevada. Identifying regions that require more research can help make more accurate predictions, design better mitigation strategies, send timely warnings, and protect public health.