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With global wildfires becoming more widespread and severe, tracking their emissions of greenhouse gases and air pollutants is becoming increasingly important. Wildfire emissions have primarily been characterized by in situ laboratory and field observations at fine scales. While this approach captures the mechanisms relating emissions to combustion phase and fuel properties, their evaluation on regional-scale plumes has been limited. In this study, we report remote observations of total column trace gases and aerosols during the 2020 wildfire season from smoke plumes in the Sierra Nevada of California with an EM27/SUN solar Fourier transform infrared (FTIR) spectrometer. We derive total column aerosol optical depth (AOD), emission factors (EFs) and modified combustion efficiency (MCE) for these fires and evaluate relationships between them, based on combustion phase at regional scales. We demonstrate that the EM27/SUN effectively detects changes in CO, CO2, and CH4 in the atmospheric column at ∼10 km horizontal scales that are attributed to wildfire emissions. These observations are used to derive total column EFCO of 120.5±12.2 and EFCH4 of 4.3±0.8 for a regional smoke plume event in mixed combustion phases. These values are consistent with in situ relationships measured in similar temperate coniferous forest wildfires. FTIR-derived AOD was compared to a nearby AERONET (AErosol RObotic NETwork) station and observed ratios of XCO to AOD were consistent with those previously observed from satellites. We also show that co-located XCO observations from the TROPOspheric Monitoring Instrument (TROPOMI) satellite-based instrument are 9.7±1.3 % higher than our EM27/SUN observations during the wildfire period. Finally, we put wildfire CH4 emissions in context of the California state CH4 budget and estimate that 213.7±49.8 Gg CH4 were emitted by large wildfires in California during 2020, about 13.7 % of the total state CH4 emissions in 2020. Our work demonstrates a novel application of the ground-based EM27/SUN solar spectrometers in wildfire monitoring by integrating regional-scale measurements of trace gases and aerosols from smoke plumes.

With global wildfires becoming more widespread and severe, tracking their emissions of greenhouse gases and air pollutants is becoming increasingly important. Wildfire emissions have primarily been characterized by in situ laboratory, and field observations at fine scales. While this approach captures the mechanisms relating emissions to combustion phase and fuel properties, their evaluation on large scale plumes has been limited. In this study, we report remote observations of total column trace gases and aerosols in the 2020 wildfire season of smoke plumes from the Sierra Nevada of California with an EM27/SUN solar Fourier transform infrared (FTIR) spectrometer. We derive total column aerosol optical depth (AOD), emission factors (EF) and modified combustion efficiency (MCE) for these fires, and evaluate relationships between them based on combustion phase at large scales. We demonstrate that the EM27/SUN effectively detects changes of CO, CO2 and CH4 in the atmospheric column at ~10 km scales that are attributed to wildfire emissions. These observations are used to derive total column EFCO of 120.5 ± 12.2 and EFCH4 of 4.3 ± 0.8 for a large smoke plume event in mixed combustion phases. These values are consistent with in situ relationships measured in similar temperate coniferous forest wildfires. FTIR derived AOD was compared to a nearby AERONET station and observed ratios of AOD to XCO were consistent with those previously observed from satellites. We also show that co-located XCO observations from the TROPOMI satellite-based instrument are 9.7 % higher than our EM27/SUN observations during the wildfire period. Finally, we put wildfire CH4 emissions in context of the California state CH4 budget and estimate that 213.7 ± 49.8 Gg CH4 were emitted by large wildfires in California during 2020, about 13.6 % of the total state CH4 emissions in 2019. Our novel application of an EM27/SUN solar spectrometer to quantify wildfire emission ratios at large scales follows predictive relationships that are consistent with in situ studies, offering promise for extensive monitoring from ground networks and satellite remote sensing.

Improved understanding of the optical properties of secondary organic aerosol (SOA) particles is needed to better predict their climate impacts. Here, SOA was produced by reacting 1-methylnaphthale...

The dairy industry in the San Joaquin Valley (SJV) is one of California’s largest methane (CH4) sources. Reducing dairy emissions is a priority for the state’s climate change plans. Observations of current dairy CH4 emissions are key to monitoring actions taken toward this goal. To help support this, we present new ground-based measurements of atmospheric column-averaged CH4 mixing ratio (XCH4) gradients across a group of 600 dairies in the central SJV using EM27/SUN solar spectrometers. We used measurements from the 2019 summer and 2020 winter seasons for a top-down emission inversion based on the WRF-STILT model. Our top-down estimates of the region’s dairy emissions range from 90% to 183% of the current CALGEM inventory’s emissions of 277Gg/yr. In contrast to the strong temperature dependence found by earlier dairy CH4 emission studies, we also find that our top-down emissions during the winter measurement days are comparable to the summer measurement days, possibly due to seasonal changes in dairy management practices and meteorological conditions. Furthermore, we find significant interday variability in our measurements and find that our emission estimates overlap with earlier top-down studies and bottom-up inventories in this region. Our study demonstrates how analysis of ground-based remotely sensed CH4 gradient observations can help improve our understanding of CH4 sources at scales relevant to mitigation policy. It also reflects the need for long-term monitoring of CH4 emissions in the region and at individual facilities to better understand their emissions.

Pollutants in tailpipe emissions can be highly elevated around roadways, and in early mornings the pollution plume can extend hundreds of meters into surrounding neighborhoods. Solid sound walls and vegetation barriers are commonly used to mitigate noise, but they also help mitigate near-road air pollution. Here we assess the effectiveness of barriers consisting of vegetation only and of a combination of vegetation and a solid sound wall (combination barrier) in reducing pollution concentrations downwind of roads, under stable atmospheric stability and calm to light wind conditions. Because there was no practical (no barrier) control site in the area, we primarily compare the two barrier types to each other and explore the importance of atmospheric conditions. Using measurements collected with a mobile platform, we develop concentration decay profiles of ultrafine and fine particles, oxides of nitrogen (NO and NO2) and carbon monoxide downwind of a freeway in California with different barrier configurations and meteorological conditions. Diurnally averaged data collected with passive samplers indicate that pollution from morning rush hour has about equal impact as the entire remainder of the day, because of differences in atmospheric dispersion as the day progresses. Under calm and stable atmospheric conditions (wind speed

California has made reducing methane (CH4) from its dairy industry, which accounts for nearly 50% of its inventoried CH4 emissions, a key part of its climate change mitigation plan. However, in situ atmospheric measurement-based estimates suggest that the state-wide dairy source may be underestimated by up to a factor of 2. Furthermore, emissions at the local scales important to mitigation policy (10’s km) are very uncertain. Ground based measurements of atmospheric column averaged methane concentrations (XCH4) can provide useful constraints on local to regional methane fluxes. Additionally, the high spatial and temporal resolution XCH4 observations by the space-based TROPOMI and GOSAT instruments provide an excellent opportunity to measure CH4 fluxes at these scales. We report field measurements of XCH4 gradients across the dairy intensive region (500 dairies, 330 Gg/yr CH4 emissions inventory) in the San Joaquin Valley (SJV) using two EM27/SUN solar spectrometers. With our EM27’s we observed several days of sustained downwind-upwind XCH4 enhancements of over 40 ppb, placing these signals well above TROPOMI and GOSAT’s precision level and among the highest reported XCH4 enhancements. We compare TROPOMI and GOSAT spatial XCH4 enhancements to our EM27 data to validate it in an area of high signal and to demonstrate its utility for observing localized sources. We also use GOSAT and TROPOMI’s data to characterize the wider SJV’s CH4 sources and to fill in temporal gaps between our field campaigns. Finally, we perform inverse optimizations using WRF-STILT simulations demonstrating that top-down observationally constrained dairy emissions are a factor of 2 larger than reported inventories. This work illustrates how ground and space-based measurements can complement each other to improve our understanding of CH4 sources at scales relevant to mitigation policy.

Methane is a potent greenhouse gas (GHG) and a critical target of climate mitigation efforts. However, actionable emission reduction efforts are complicated by large uncertainties in the methane budget at relevant scales. Here, we present Vista, a Geographic Information System (GIS)-based approach to map potential methane emissions sources in greater Los Angeles, an area with a dense, complex mixture of sources. The goal of this work is to provide a database that, together with atmospheric observations, improves methane emissions estimates in urban areas with complex infrastructure. We aggregated methane source location information into three sectors (energy, agriculture, and waste) following the frameworks used by the State of California GHG Inventory and the IPCC Guidelines for GHG Reporting. Geospatial modelling was applied to publicly available datasets to precisely geolocate facilities and infrastructure comprising major anthropogenic methane source sectors. The final database, Vista-Los Angeles (LA), is presented as maps of infrastructure known or expected to emit methane. Vista-LA contains over 33,000 features concentrated on