The California Air Resources Board (CARB or ARB) is the "clean air agency" of the government of California. Established in 1967 when then-governor Ronald Reagan signed the Mulford-Carrell Act, combining the Bureau of Air Sanitation and the Motor Vehicle Pollution Control Board, CARB is a department within the cabinet-level California Environmental Protection Agency.The stated goals of CARB include attaining and maintaining healthy air quality; protecting the public from exposure to toxic air contaminants; and providing innovative approaches for complying with air pollution rules and regulations. CARB has also been instrumental in driving innovation throughout the global automotive industry through programs such as its ZEV mandate.One of CARB's responsibilities is to define vehicle emissions standards. California is the only state permitted to issue emissions standards under the federal Clean Air Act, subject to a waiver from the United States Environmental Protection Agency. Other states may choose to follow CARB or the federal vehicle emission standards but may not set their own.
Atmospheric boundary layer simulations in weather models, important elements of air quality simulations, are coupled with land surface parameterizations. The San Joaquin Valley (SJV) of California and the Multi-state Mid-Atlantic (MMA) feature diverse land uses, including agriculture, urban areas, and forests, which pose challenges for simulating surface fluxes. This study evaluates surface fluxes in the Weather Research and Forecasting (WRF) model using physical configurations adopted by state air quality agencies in California and Pennsylvania. We compared WRF simulations with year-long eddy-covariance flux measurements from 16 sites across the two regions. Results show that the Pleim-Xiu land surface model (PX LSM) exhibits substantial heat flux biases in the SJV but lacks systematic biases in the MMA. In the SJV, the model overestimates daytime (10:00-16:00 LST) sensible heat flux (H) by 260 W m-2 (274%) and underestimates latent heat flux (LE) by 200 W m-2 (68%) at irrigated croplands and orchards during spring and summer. In the MMA, PX LSM moderately overestimates both H and LE, with stronger partitioning into H over urban surfaces and into LE over vegetation. Daytime momentum fluxes are overestimated in both regions, while nighttime biases are inconsistent. Our findings suggest that in the SJV, heat flux biases are strongly associated with irrigation during the growing season, while in the MMA, model-data residuals are limited to modest errors in the Bowen ratio and depend on land cover. Improving WRF’s representation of irrigation and land use, potentially through satellite remote sensing, may enhance surface flux simulation accuracy.
Urban agriculture plays a key role in urban ecosystems functionality and resilience, and can take many forms: allotment gardens, community garden or urban microfarms. In the latest, urban microfarms can be developed at the soil surface but also on rooftops because of the scarcity of space. The soils of these urban microfarms, either pseudo-natural or Technosol, are also characterised by a high variability in their biological and geochemical properties, influencing their ability to support soil biodiversity. This study investigated the biodiversity (taxonomic richness and abundance) of microorganisms, mesofauna, macrofauna and plant and linked it to soil geochemical parameters in urban microfarm soils. Biological and soil samplings and vegetation identifications were conducted in 12 plots of urban microfarms of the Paris region (France), including 7 plots on the ground level and 5 on rooftops, with characterisation of microbial, collembolan, macrofauna and plant communities and abiotic parameters. There was a very high intra- and inter-site variability within urban microfarms soils, regardless of the taxonomic group considered. Ground microfarms appeared to favour abundance and diversity of spontaneous plants and macrofauna, while rooftop microfarms seemed to harbour more microorganisms (especially bacteria) and Collembola, which were also more diverse, in relation with the characteristics of the two types of soil. Based on our findings we share recommendation for future studies supporting the development of urban agriculture.
We evaluated the three-dimensional (3D) structure, composition, and biomass of understory fuels common to prescribed burn programs in pine-dominated forests of the southeastern and western United States. Traditional fuel characterization in these systems has been limited to two-dimensional representations, including biomass, height, and cover estimates per unit area. The objective of this study was to compare close-range photogrammetry with terrestrial lidar scanning (TLS), validated by destructive sampling to inform gridded 3D maps of live and dead understory fuels. We developed and analyzed a large dataset of calibrated plots using co-located terrestrial lidar scanning (TLS), close-range photogrammetry, and destructive biomass sampling conducted at 18 field sites across the southeastern and western US. Field sites represented vegetation commonly burned in prescribed fire programs, including southeastern mesic flatwood forests, southeastern loblolly-sweetgum forests, western ponderosa pine and mixed conifer forests, and western grasslands. Scanning methods were compared with metrics derived from fine-scale volumetric fuels sampling to determine the most effective method for mapping fuels in 3D. TLS and SfM-based point cloud metrics were variable in their accuracy dependent on vegetation type and levels of occlusion in understory vegetation, and neither was effective at predicting fuels within the lowest sampled stratum (0–10 cm). Modeled relationships between photogrammetry and TLS-based metrics and fuel biomass and bulk density can be used to produce unit-scale mapping for prescribed burn programs that are informed by the 3D structure and composition of understory fuels. However, point-cloud metrics may be limited in their utility in dense fuel complexes with high levels of occlusion. This work supports advances in the characterization and mapping of wildland fuel beds for use in physics-based models of fire behavior and effects that rely on gridded, 3D inputs.
Accurate quantification of methane total emission and sectoral contribution is essential for climate prediction and effective mitigation strategies. Recent advances in remote sensing have significantly improved the spatial and temporal coverage of methane observations globally, which enables more robust evaluation of existing emission estimates and provides critical observational constraints in the regions where ground-based measurements are sparse. We established a TROPOMI satellite data-based assimilation framework within the WRF-Chem/Chem-DART system to evaluate and optimize surface methane emission estimates. Using the joint assimilation capabilities of Chem-DART, we assimilate TROPOMI-CH 4 retrievals and surface and upper-air meteorological observations simultaneously, to more realistic representation of mesoscale atmospheric transport and boundary-layer meteorology while constraining methane emissions at high spatial and temporal resolutions. In this study, we applied the framework to constrain summer-time methane emissions in California, where active emission sources are distributed across a region characterized by complex terrain and meteorology, with favorable satellite coverage. The estimated statewide weekly methane emissions during summer range from 1.5 to 3.0 Tg yr⁻¹. The optimized surface emission estimates better reproduced independent tower-based methane concentration measurements than simulations using the default emission inventory. In addition, our regional emission estimates are consistent with independent previous top-down studies in terms of both total methane emissions and major sectoral contributions. These results highlight the framework’s capability to characterize methane emission dynamics and source attribution in detail and provide robust regional high-resolution methane emission estimates.
Commercial kitchens are often characterized by harsh working environments, which are linked to high staff turnover as evidenced by limited measurement data and anecdotal reports. These kitchens are energy-intensive, and equipment upgrades that improve indoor environmental conditions and lower energy costs may be possible. In 2024, we recruited 10 independently operated restaurants in California to participate in a week-long sampling study. Our goal was to assess indoor environmental quality, including thermal conditions and indoor air pollutant concentrations, and identify opportunities for energy-saving measures, such as improved kitchen ventilation and efficient cooking appliances. Here, we present a subset of the indoor air pollutant measurements, namely volatile organic compounds (VOCs) and polycyclic aromatic hydrocarbons (PAHs), that were measured on one day during the week-long sampling period in each kitchen. In addition to VOCs and PAHs, our study also monitored other indoor air pollutants, such as ultrafine particles and nitrogen oxides, to capture the full range of indoor air pollutants present. VOCs and PAHs were measured in each kitchen near where most cooking activities occurred. Our results show sum of VOCs to range between 142 to 519 μg/m3, with a mean concentration of 277 μg/m3. Aldehyde, alcohol, terpenes, and ketone are the key groups of VOCsdetected, accounting for 46 to 88% (mean = 72%) of the total VOC concentrations measured. Aside from cooking related emissions, other sources of VOCs are evident from the presence of specific groups of VOCs found in some of our samples, such as siloxane from personal care products and glycol ethers likely associated with cleaning. The sum of 23 PAHs range between 19 to 1103 ng/m3, with a mean concentration of 378 ng/m3. Our results suggested higher PAHs in kitchens of larger restaurants with more cooking activities compared to the small establishments. While we are unable to draw conclusions about the levels of VOCs and PAHs with the design and performance of exhaust hoods, we noticed that in one of the kitchens with relatively higher PAHs, using an unlisted exhaust hood with low airflow rate is likely a contributing factor.
