Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15-70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under-sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups.

Methane is both a significant and short-lived greenhouse gas compared to CO2, and reducing methane emissions from natural gas distribution systems may offer cost-effective reduction opportunities. We report substantial new direct leak rate measurements from customer meter set assemblies (MSAs) in Southern California. In a novel way, emission factors are defined in terms of aboveground Hazardous and Nonhazardous leak categories, which take direct advantage of readily available industry leak data. We also studied leaks that were not detected as part of normal leak survey procedures. As a result, this yields company-specific emission factors that can be used to track progress in reducing methane emissions. This approach also has the advantage of explicitly accounting for the skewed or fat-tail distribution of leak rates by treating high flow rate MSA leaks separately from low flow rate MSA leaks. The Southern California Gas (SoCalGas) methane emission factors, based on 485 leak rate measurements by direct enclosure, were 4.55 (95% confidence interval: 2.32 to 7.14) kg/day for Hazardous leaks, 0.149 (0.119 to 0.183) kg/day for Nonhazardous leaks, and 0.0039 (0.0003 to 0.0198) kg/day for Non-Detected leaks. The percentage of surveyed meters with nondetected leaks was 29.1% (24.3 to 34.6%). Based on a robust Monte Carlo analysis, total leak emissions from MSAs for the SoCalGas system were reduced by 35% based on data from 2015 to 2022. These reductions were attributed to surveying a larger number of MSAs and accelerated leak repair rates. In traditional population-based emission inventories, an individual emission factor for a given asset category is multiplied by the total population of MSAs within the category. This approach simply cannot capture the reduction in leak numbers and methane emissions resulting from leak mitigation and prevention programs.

ABSTRACT In the beginning of 2020, the global human population encountered the pandemic of novel coronavirus disease 2019 (COVID-19). Despite social and economic concerns, this epidemiologic emergency has brought unexpected positive consequences for environmental quality as human activities were reduced. In this paper, the impact of restricted human activities on urban air quality in Ecuador is investigated. This country implemented a particularly strict set of quarantine measures at the very dawn of the exponential growth of infections on March 17, 2020. As a result, significant reductions in the concentrations of NO2 (–68%), SO2 (–48%), CO (–38%) and PM2.5 (–29%) were measured in the capital city of Quito during the first month of quarantine. This large drop in air pollution concentrations occurred at all the monitoring sites in Quito, serving as a valuable proof of the anthropogenic impact on urban air quality. The spatial evolution of atmospheric pollution using observed surface and satellite data, showed different results for the two major cities: Quito and Guayaquil. While the population in Quito adhered to the quarantine measures immediately, in the port city of Guayaquil, quarantine measures were slow to be adopted and, thus, the effect on air quality in Guayaquil occurred more slowly. This lag could have a considerable cost to the mortality rate in the port city, not only due to the spread of the disease but also due to the poor air quality. Overall, the air quality data demonstrate how quickly air quality can improve when emissions are reduced.

Atmospheric correction (AC) is necessary for deriving accurate spaceborne estimates of surface reflectance (SR) for agricultural fields. In this effort, we evaluated the consistency of various AC tools applied to WorldView-3 (WV3) and PRISMA imagery including MODTRAN, FLAASH, and ASI’s Level2D AC. Overall, these AC tools produced consistent SR estimates compared to top-of-atmosphere reflectance (TOAR). MODTRAN and FLAASH were highly consistent when applied to WV3 imagery, yielding SR relative percent differences <10%, and SR value differences <|0.005| for all bands except the coastal blue. For a case study of cover-cropped fields, normalized difference vegetation index (NDVI) values derived from PRISMA and WV3 SR imagery showed only slight variation between different AC tools (|0.00| to |0.05|). Observed NDVI differences between TOAR and SR (|0.06| to |0.20|) and change in NDVI from cover crop termination (up to |0.70|) were greater than AC tool differences. For a crop residue case study, shortwave infrared imagery was converted to spectral indices (SIs) and used for fractional crop residue cover (f R ) estimation. The shortwave infrared normalized difference residue index (SINDRI) demonstrated good f R estimation performance (R 2 = 0.88) with minimal f R RMSE differences (<0.005) for various AC tools, image sources, and SR vs. TOAR. SIs computed with a 2100 nm band, like the cellulose absorption index (CAI) yielded top performance in f R (R 2 = 0.89 to 0.90) but could only be derived from PRISMA imagery. Although SIs covering the wavelength range of 2035-2265 nm performed well, the lignin cellulose absorption index (LCA) - featuring a 2330 nm band - produced larger f R RMSE differences (0.018 for AC tools and 0.035 for SR vs. TOAR). For SINDRI, AC tool type did not impact f R estimation substantially, but AC was found to be critical for deriving stable coefficients for SI-to-f R calibrations, a finding that highlights the importance of AC for time series f R applications like tillage monitoring.

Air quality in the Pacific Northwest (PNW) of the U.S has generally been good in recent years, but unhealthy events were observed due to wildfires in summer or wood burning in winter. The current air quality forecasting system, which uses chemical transport models (CTMs), has had difficulty forecasting these unhealthy air quality events in the PNW. We developed a machine learning (ML) based forecasting system, which consists of two components, ML1 (random forecast classifiers and multiple linear regression models) and ML2 (two-phase random forest regression model). Our previous study showed that the ML system provides reliable forecasts of O-3 at a single monitoring site in Kennewick, WA. In this paper, we expand the ML forecasting system to predict both O-3 in the wildfire season and PM2.5 in wildfire and cold seasons at all available monitoring sites in the PNW during 2017-2020, and evaluate our ML forecasts against the existing operational CTM-based forecasts. For O-3, both ML1 and ML2 are used to achieve the best forecasts, which was the case in our previous study: ML2 performs better overall (R-2 = 0.79), especially for low-O-3 events, while ML1 correctly captures more high-O-3 events. Compared to the CTM-based forecast, our O-3 ML forecasts reduce the normalized mean bias (NMB) from 7.6 to 2.6% and normalized mean error (NME) from 18 to 12% when evaluating against the observation. For PM2.5, ML2 performs the best and thus is used for the final forecasts. Compared to the CTM-based PM2.5, ML2 clearly improves PM2.5 forecasts for both wildfire season (May to September) and cold season (November to February): ML2 reduces NMB (-27 to 7.9% for wildfire season; 3.4 to 2.2% for cold season) and NME (59 to 41% for wildfires season; 67 to 28% for cold season) significantly and captures more high-PM2.5 events correctly. Our ML air quality forecast system requires fewer computing resources and fewer input datasets, yet it provides more reliable forecasts than (if not, comparable to) the CTM-based forecast. It demonstrates that our ML system is a low-cost, reliable air quality forecasting system that can support regional/local air quality management.

A machine learning (ML) based modeling framework has been successfully used to provide operational forecasts of O3 at Kennewick, WA. This paper shows its performance when applied to other observation locations to predict O3 and PM2.5 concentrations. The 10-time, 10-fold cross-validation method was used to evaluate the model performance in the Pacific Northwest (PNW). Similar to Kennewick, ML1 captures more high-O3 events, but also generates more false alarms, and the accuracy of ML2 is better (R2 = 0.79), especially for low-O3 events. Compared to AIRPACT, the combined modeling framework reduces the normalized mean bias (NMB) from 7.6% to 2.6%. In terms of Air Quality Index (AQI) forecasts, improvements occur for each AQI level which reflects more accurate O3 predictions and better capture of more high-O3 events. For PM2.5, ML1 and ML2 demonstrate similar capabilities to predict high-PM2.5 events and ML2 keeps its accuracy for low-PM2.5 predictions, so there is no need to combine the two methods. During the evaluation period, AIRPACT under-predicts the wildfire season PM2.5 concentrations in the PNW (NMB = -27%) and over-predicts at some sites in the cold season up to 200%, while ML2 has a lower NMB in both seasons (NMB = 7.9% in the wildfire season and 2.2% in the cold season) and correctly captures more high-PM2.5 events. The ML modeling framework is now operational for forecasts of O3 and PM2.5 at over 100 observation sites in the PNW.

In the light of the 21st century, after two devastating world wars, humanity still has not learned to solve their conflicts through peaceful negotiations and dialogue. Armed conflicts, both international and within a single state, still cause devastation, displacement, and death all over the world. Not to mention the consequences that war has on the environment. Due to a lack of published research about war impact on modern air quality, this work studies air pollution evolution during the first months of the Russian-Ukrainian conflict. Satellite images of NO2, CO, O-3, SO2, and PM2.5 over Ukrainian territory and PM2.5 land monitoring data for Kyiv were analyzed. The results showed that NO2 and PM2.5 correlated the most with war activities. CO and O3 levels increased, while SO2 concentrations reduced four-fold as war intensified. Drastic increases in pollution (especially PM2.5) from bombing and structural fires, raise additional health concerns, which might have serious implications for the exposed local and regional populations. This study is an invaluable proof of the impact any armed conflict has on air quality, the population, and environment.

Chemical transport models (CTMs) are widely used for air quality forecasts, but these models require large computational resources and often suffer from a systematic bias that leads to missed poor air pollution events. For example, a CTM-based operational forecasting system for air quality over the Pacific Northwest, called AIRPACT, uses over 100 processors for several hours to provide 48-h forecasts daily, but struggles to capture unhealthy O3 episodes during the summer and early fall, especially over Kennewick, WA. This research developed machine learning (ML) based O3 forecasts for Kennewick, WA to demonstrate an improved forecast capability. We used the 2017–2020 simulated meteorology and O3 observation data from Kennewick as training datasets. The meteorology datasets are from the Weather Research and Forecasting (WRF) meteorological model forecasts produced daily by the University of Washington. Our ozone forecasting system consists of two ML models, ML1 and ML2, to improve predictability: ML1 uses the random forest (RF) classifier and multiple linear regression (MLR) models, and ML2 uses a two-phase RF regression model with best-fit weighting factors. To avoid overfitting, we evaluate the ML forecasting system with the 10-time, 10-fold, and walk-forward cross-validation analysis. Compared to AIRPACT, ML1 improved forecast skill for high-O3 events and captured 5 out of 10 unhealthy O3 events, while AIRPACT and ML2 missed all the unhealthy events. ML2 showed better forecast skill for less elevated-O3 events. Based on this result, we set up our ML modeling framework to use ML1 for high-O3 events and ML2 for less elevated O3 events. Since May 2019, the ML modeling framework has been used to produce daily 72-h O3 forecasts and has provided forecasts via the web for clean air agency and public use: http://ozonematters.com/. Compared to the testing period, the operational forecasting period has not had unhealthy O3 events. Nevertheless, the ML modeling framework demonstrated a reliable forecasting capability at a selected location with much less computational resources. The ML system uses a single processor for minutes compared to the CTM-based forecasting system using more than 100 processors for hours.

The magnitude of ecosystem services provided by winter cover crops is linked to their performance (i.e., biomass and associated nitrogen content, forage quality, and fractional ground cover), although few studies quantify these characteristics across the landscape. Remote sensing can produce landscape-level assessments of cover crop performance. However, commonly employed optical vegetation indices (VI) saturate, limiting their ability to measure high-biomass cover crops. Contemporary VIs that employ red-edge bands have been shown to be more robust to saturation issues. Additionally, synthetic aperture radar (SAR) data have been effective at estimating crop biophysical characteristics, although this has not been demonstrated on winter cover crops. We assessed the integration of optical (Sentinel-2) and SAR (Sentinel-1) imagery to estimate winter cover crops biomass across 27 fields over three winter–spring seasons (2018–2021) in Maryland. We used log-linear models to predict cover crop biomass as a function of 27 VIs and eight SAR metrics. Our results suggest that the integration of the normalized difference red-edge vegetation index (NDVI_RE1; employing Sentinel-2 bands 5 and 8A), combined with SAR interferometric (InSAR) coherence, best estimated the biomass of cereal grass cover crops. However, these results were season- and species-specific (R2 = 0.74, 0.81, and 0.34; RMSE = 1227, 793, and 776 kg ha−1, for wheat (Triticum aestivum L.), triticale (Triticale hexaploide L.), and cereal rye (Secale cereale), respectively, in spring (March–May)). Compared to the optical-only model, InSAR coherence improved biomass estimations by 4% in wheat, 5% in triticale, and by 11% in cereal rye. Both optical-only and optical-SAR biomass prediction models exhibited saturation occurring at ~1900 kg ha−1; thus, more work is needed to enable accurate biomass estimations past the point of saturation. To address this continued concern, future work could consider the use of weather and climate variables, machine learning models, the integration of proximal sensing and satellite observations, and/or the integration of process-based crop-soil simulation models and remote sensing observations.

This study focused on optimizing the placement of shortwave infrared (SWIR) bands for pixel-level estimation of fractional crop residue cover (fR) for the upcoming Landsat Next mission. We applied an iterative wavelength shift approach to a database of crop residue field spectra collected in Beltsville, Maryland, USA (n = 916) and computed generalized two- and three-band spectral indices for all wavelength combinations between 2000 and 2350 nm, then used these indices to model field-measured fR. A subset of the full dataset with a Normalized Difference Vegetation Index (NDVI) < 0.3 threshold (n = 643) was generated to evaluate green vegetation impacts on fR estimation. For the two-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized normalized difference using 2226 nm and 2263 nm bands produced the top fR estimation performance (R2 = 0.8222; RMSE = 0.1296). These findings were similar to the established two-band Shortwave Infrared Normalized Difference Residue Index (SINDRI) (R2 = 0.8145; RMSE = 0.1324). Performance of the two-band generalized normalized difference and SINDRI decreased for the full-NDVI dataset (R2 = 0.5865 and 0.4144, respectively). For the three-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized ratio-based index with a 2031–2085–2216 nm band combination, closely matching established Cellulose Absorption Index (CAI) bands, was top performing (R2 = 0.8397; RMSE = 0.1231). Three-band indices with CAI-type wavelengths maintained top fR estimation performance for the full-NDVI dataset with a 2036–2111–2217 nm band combination (R2 = 0.7581; RMSE = 0.1548). The 2036–2111–2217 nm band combination was also top performing in fR estimation (R2 = 0.8690; RMSE = 0.0970) for an additional analysis assessing combined green vegetation cover and surface moisture effects. Our results indicate that a three-band configuration with band centers and wavelength tolerances of 2036 nm (±5 nm), 2097 nm (±14 nm), and 2214 (±11 nm) would optimize Landsat Next SWIR bands for fR estimation.

This two-part study explores hyperspectral (300–700 nm) aerosol optical measurements obtained from in situ sampling methods employed during the May–June 2016 Korea–United States Ocean Color (KORUS-OC) cruise conducted in concert with the broader air quality campaign (KORUS-AQ). Part 1 focused on the hyperspectral measurement of extinction coefficients (σext) using the recently developed in situ Spectral Aerosol Extinction (SpEx) instrument and showed that second-order polynomials provided a better fit to the measured spectra than power law fits. Two dimensional mapping of the second-order polynomial coefficients (a1, a2) was used to explore the information content of the spectra. Part 2 expands on that work by applying a similar analytical approach to filter-based measurements of aerosol hyperspectral total absorption (σabs) and soluble absorption from filters extracted with either deionized water (σDI-abs) or methanol (σMeOH-abs). As was found for σext, second-order polynomials provided a better fit to all three absorption spectra sets. Averaging the measured σext from Part 1 over the filter sampling intervals in this work, hyperspectral single-scattering albedo (ω) was calculated. Water-soluble aerosol composition from the DI extracts was used to examine relationships with the various measured optical properties. In particular, both σDI-abs(365 nm) and σMeOH-abs(365 nm) were found to be best correlated with oxalate (C2O42-), but elevated soluble absorption was found from two chemically and optically distinct populations of aerosols. The more photochemically aged aerosols of those two groups exhibited partial spectra (i.e., the longer wavelengths of the spectral range were below detection) while the less-aged aerosol of the other group exhibited complete spectra across the wavelength range. The chromophores of these groups may have derived from different sources and/or atmospheric processes, such that photochemical age may have been only one factor contributing to the differences in the observed spectra. The differences in the spectral properties of these groups was evident in (a1, a2) maps. The results of the two-dimensional mapping shown in Parts 1 and 2 suggest that this spectral characterization may offer new methods to relate in situ aerosol optical properties to their chemical and microphysical characteristics. However, a key finding of this work is that mathematical functions (whether power laws or second-order polynomials) extrapolated from a few wavelengths or a subrange of wavelengths fail to reproduce the measured spectra over the full 300–700 nm wavelength range. Further, the σabs and ω spectra exhibited distinctive spectral features across the UV and visible wavelength range that simple functions and extrapolations cannot reproduce. These results show that in situ hyperspectral measurements provide valuable new data that can be probed for additional information relating in situ aerosol optical properties to the underlying physicochemical properties of ambient aerosols. It is anticipated that future studies examining in situ aerosol hyperspectral properties will not only improve our ability to use optical data to characterize aerosol physicochemical properties, but that such in situ tools will be needed to validate hyperspectral remote sensors planned for space-based observing platforms.

In situ observations of spectrally resolved aerosol extinction coefficients (300–700 nm at ∼ 0.8 nm resolution) from the May–June 2016 Korea–United States Ocean Color (KORUS-OC) oceanographic field campaign are reported. Measurements were made with the custom-built Spectral Aerosol Extinction (SpEx) instrument that previously has been characterized only using laboratory-generated aerosols of known size and composition. Here, the performance of SpEx under realistic operating conditions in the field was assessed by comparison to extinction coefficients derived from commercial instruments that measured scattering and filter-based absorption coefficients at three discrete visible wavelengths. Good agreement was found between these two sets of extinction coefficients with slopes near unity for all three wavelengths within the SpEx measurement error (± 5 Mm−1). The meteorological conditions encountered during the cruise fostered diverse ambient aerosol populations with varying sizes and composition at concentrations spanning 2 orders of magnitude. The sampling inlet had a 50 % size cut of 1.3 µm diameter particles such that the in situ aerosol sampling suite deployed aboard ship measured fine-mode aerosols only. The extensive hyperspectral extinction data set acquired revealed that nearly all measured spectra exhibited curvature in logarithmic space, such that Ångström exponent (α) power law fits could lead to large errors compared to measured values. This problem was particularly acute for α values calculated over only visible wavelengths and then extrapolated to the UV, highlighting the need for measurements in this wavelength range. Second-order polynomial fits to the logarithmically transformed data provided a much better fit to the measured spectra than the linear fits of power laws. Building on previous studies that used total column aerosol optical depth observations to examine the information content of spectral curvature, the relationship between α and the second-order polynomial fit coefficients (a1 and a2) was found to depend on the wavelength range of the spectral measurement such that any given α maps into a line in (a1, a2) coefficient space with a slope of −2LN(λch), where λch is defined as the single wavelength that characterizes the wavelength range of the measured spectrum (i.e., the “characteristic wavelength”). Since the curvature coefficient values depend on λch, it must be taken into account when comparing values from spectra obtained from measurement techniques with different λch. Previously published work has shown that different bimodal size distributions of aerosols can exhibit the same α yet have differing spectral curvature with different (a1, a2). This implies that (a1, a2) contain more information about size distributions than α alone. Aerosol size distributions were not measured during KORUS-OC, and the data reported here were limited to the fine fraction, but the (a1, a2) maps obtained from the SpEx data set are consistent with the expectation that (a1, a2) may contain more information than α – a result that will be explored further with future SpEx and size distribution data sets.

Earth Observing Systems (EOS) are a valuable tool for vegetation monitoring. Coupling information from different spaceborne sensors allows reducing the revisit time and a more accurate Earth surface processes monitoring. However, combining surface reflectance (SR) from different EOS requires compensating for atmospheric disturbances and viewing angle induced effects. The objectives of this study were i) to compare the atmospherically corrected Sentinel-2 (S2) and WorldView-3 (WV3) SR with ground-truth spectra acquired with a FieldSpec and ii) to propose and validate a signal normalization procedure for coupling S2 and WV3 imagery. The S2 imagery showed a reliable match with field spectra, however SR from WV3 was significantly different. These discrepancies likely resulted from the off-nadir acquisition angles. The signal normalization procedure, based on calibrating each WV3 band against ground-truth spectra, allowed an accurate coupling of S2 and WV3 imagery ($\text{RMSE} < 0.007$ in visible bands and $\text{RMSE}=0.018$ in the NIR band).

A bias correction scheme based on a Kalman filter (KF) method has been developed and implemented for the AIRPACT air quality forecast system which operates daily for the Pacific Northwest. The KF method was used to correct hourly rolling 24-h average PM2.5 concentrations forecast at each monitoring site within the AIRPACT domain and the corrected forecasts were evaluated using observed daily PM2.5 24-h average concentrations from 2017 to 2018. The evaluation showed that the KF method reduced mean daily bias from approximately -50% to ±6% on a monthly averaged basis, and the corrected results also exhibited much smaller mean absolute errors typically less than 20%. These improvements were also apparent for the top 10 worst PM2.5 days during the 2017-2018 test period, including months with intensive wildfire events. Significant differences in AIRPACT performance among urban, suburban, and rural monitoring sites were greatly reduced in the KF bias correction forecasts. The daily 24-h average bias corrections for each monitoring site were interpolated to model grid points using three different interpolation schemes: cubic spline, Gaussian Kriging, and linear Kriging. The interpolated results were more accurate than the original AIRPACT forecasts, and both Kriging methods were better than the cubic spline method. The Gaussian method yielded smaller mean biases and the linear method yielded smaller absolute errors. The KF bias correction method has been implemented operationally using both Kriging interpolation methods for routine output on the AIRPACT website (http://lar.wsu.edu/airpact). This method is relatively easy to implement, but very effective to improve air quality forecast performance.Implications: Current chemical transport models, including CMAQ, used for air quality forecasting can have large errors and uncertainties in simulated PM2.5 concentrations. In this paper, we describe a relatively simple bias correction scheme applied to the AIRPACT air quality forecast system for the Pacific Northwest. The bias correction yields much more accurate and reliable PM2.5 results compared to the normal forecast system. As such, the operational bias corrected forecasts will provide a much better basis for daily air quality management by agencies within the region. The bias corrected results also highlight issues to guide further improvements to the normal forecast system.

The Air Indicator Report for Public Awareness and Community Tracking (AIRPACT) is a comprehensive, automated air quality forecast system that provides 48-hr in-advance air quality over the Pacific Northwest region (http://lar.wsu.edu/airpact/). Since 2001, the AIRPACT forecasting system has been successfully operated by Washington State University, with the financial support from the Northwest International Air Quality and Environmental Science consortium (NW-AIRQUEST). AIRPACT consists of the Sparse Matrix Operator Kernel Emissions (SMOKE) model to provide temporal and spatial emissions, the Community Multiscale Air Quality (CMAQ) model to simulate hourly ozone, particulate matter and related precursor concentrations over the Pacific Northwest region, and the Weather Research and Forecasting (WRF) model to simulate meteorology fields which are inputs for CMAQ: WRF is run by University of Washington and their outputs are transferred to Washington State University. AIRPACT is one of the longest operational regional air quality forecast system in the US that is based on a chemical transport modeling. In this paper, we have evaluated AIRPACT forecasts for the last ten years (2009-2018) against quality-controlled EPA Air Quality System observations, with particular focus on examining how overall air quality forecast skill has changed as the AIRPACT system has evolved. During this period, AIRPACT has been intermittently updated with improved physical and chemical processes as well as newer emissions and higher resolution model domains. Our evaluation results show that AIRPACT's skill at forecasting ozone (O3) has improved over time. However, the fine particulate matter (PM2.5), forecast performance has decreased over time. The PM2.5 forecasts in the most recent version of AIRPACT were underpredicted to a larger degree than the previous version, partly because elevated PM2.5 concentrations during the wildfire season in the years 2015 and 2018 were underestimated. In order to improve overall air quality forecast accuracy, our future efforts should focus on building a more reliable forecast system to handle extreme air quality events in combination with using new techniques for data-assimilation, ensemble forecasting, and statistical post-processing.

Particulate matter (PM) pollution is associated with adverse effects on human health and the environment. There is no designated PM(2.5)emission factor for horizontal grain conveyors. Instead, in Washington state, the air permitting agency uses an emission factor for headhouse and grain handling operations to issue permits. There is concern that this factor does not accurately represent the conveyor operations and limits the size and operation of wheat pile facilities. The primary goal of this work was to estimate the PM(2.5)emission rate (which can further be converted to an emission factor) from wheat conveying operations at a large wheat pile storage facility in eastern Washington using an atmospheric tracer ratio method, with CO(2)gas as the tracer. The field study results yield an emission rate of 5.2 +/- 1.7 grams of PM(2.5)per hour and these emissions are due to the transfer point from an upper belt to a lower belt. This rate is approximately 320 times lower than the emission rate for headhouse operations which has been used previously to represent conveyor operations. The emission rate was in relatively good agreement with results of an inverse Gaussian plume model calculation of emissions using measured ambient PM(2.5)levels at a very short distance downwind of the transfer point. A consistent PM(2.5)to tracer gas ratio over the tests showed that PM(2.5)and CO(2)disperse in a similar manner and confirmed that the CO(2)tracer release was a reliable simulation of the PM(2.5)pollutant source over distances involved in the study (less than 10 meters). The results also indicate a need for the Environmental Protection Agency to develop a designated PM(2.5)emission factor for wheat conveyance. Implications There are presently no emission factors available for large wheat pile storage facilities where wheat is transferred via long horizontal conveyor belts. As a result, local and state permitting agencies use emission factors for other types of grain handling systems. In this paper, we report the first measurements of PM(2.5)emission rates (that can further be converted to emission factors using a known grain rate on the conveyor) for horizontal grain conveyors used at wheat pile storage facilities. The measured emission rate is much less than the emission rate derived from the surrogate emission factor currently used for permit purposes. This has implications for the size and operation of wheat pile storage facilities.

Whole-house emission rates and indoor loss coefficients of formaldehyde and other volatile organic compounds (VOCs) were determined from continuous measurements inside a net-zero energy home at two different air change rates (ACHs). By turning the mechanical ventilation on and off, it was demonstrated that formaldehyde concentrations reach a steady state much more quickly than other VOCs, consistent with a significant indoor loss rate attributed to surface uptake. The first order loss coefficient for formaldehyde was 0.47 ± 0.06 h-1 at 0.08 h-1 ACH and 0.88 ± 0.22 h-1 at 0.62 h-1 ACH. Loss rates for other VOCs measured were not discernible, with the exception of hexanoic acid. A factor of 5.5 increase in the ACH increased the whole-house emission rates of VOCs but by varying degrees (factors of 1.1 to 3.8), with formaldehyde displaying no significant change. The formaldehyde area-specific emission rate (86 ± 8 μg m-2 h-1) was insensitive to changes in the ACH because its large indoor loss rate muted the impact of ventilation on indoor air concentrations. These results demonstrate that formaldehyde loss rates must be taken into account to correctly estimate whole-house emission rates and that ventilation will not be as effective at reducing indoor formaldehyde concentrations as it is for other VOCs.

During the Regional Approaches to Climate Change (REACCH) program, eddy covariance monitoring over agricultural fields were used to estimate annual carbon and water budgets in the inland Pacific Northwest. Here, we assess the effect of a bias in the high-frequency CO2 concentration measurements using the Campbell Scientific EC150 infra-red gas analyzer on the CO2 fluxes and field-scale carbon balances. The bias stems from using a lower frequency temperature measurement to calculate the CO2 density, which misses higher frequency temperature fluctuations. To generate the bias adjustment, data were collected over four similar agricultural sites as part of the Long-Term Agroecosystem Research network for multiple months using the same four instrument sets used in the REACCH project. The difference between the high-frequency and low-frequency CO2 fluxes were regressed against the kinematic heat fluxes to generate a correction equation for each instrument set, which were applied to the historical REACCH data to determine the effect of the bias on the measured and gap-filled flux values. The re-calculated positive biases in the measured fluxes were 40 gC-CO(2)m(-2) yr(-1) to 126 gC-CO(2)m(-2) yr-1, indicating greater losses to the atmosphere than initially estimated. Once gap-filled, three out of fourteen site-years switched from weak carbon sinks to weak carbon sources. When the carbon exported via harvest was included in the budget calculation the bias correction still impacted the source/sink strength but did not change the sign of the carbon balance. Overall, the total net ecosystem exchange decreased between 300-470 gC-CO(2)m(-2) per site (29-46%) over the 4 crop-years from the bias adjustment process.

With the addition of nitrogen (N), agricultural soils are the main anthropogenic source of N2O, but high spatial and temporal variabilities make N2O emissions difficult to characterize at the field scale. This study used flux-gradient measurements to continuously monitor N2O emissions at two agricultural fields under different management regimes in the inland Pacific Northwest of Washington State, USA. Automated 16-chamber arrays were also deployed at each site; chamber monitoring results aided the interpretation of the flux gradient results. The cumulative emissions over the six-month (1 April-30 September) monitoring period were 2.4 ± 0.7 and 2.1 ± 2 kg N2O-N/ha at the no-till and conventional till sites, respectively. At both sites, maximum N2O emissions occurred following the first rainfall event after N fertilization, and both sites had monthlong emission pulses. The no-till site had a larger N2O emission factor than the Intergovernmental Panel on Climate Change Tier 1 emission factor of 1% of the N input, while the conventional-till site's emission factor was close to 1% of the N input. However, these emission factors are likely conservative. We estimate that the global warming potential of the N2O emissions at these sites is larger than that of the no-till conversion carbon uptake. We recommend the use of chambers to investigate spatiotemporal controls as a complementary method to micrometeorological monitoring, especially in systems with high variability. Continued monitoring coupled with the use of models is necessary to investigate how changing management and environmental conditions will affect N2O emissions.

Management of livestock manure may recycle nutrients and decrease greenhouse gas (GHG) and ammonia (NH3) emissions. The objectives were to ascertain effects of environmental conditions and turning on methane (CH4), nitrous oxide (N2O), and NH3 emissions and if treatment with 8.5 g of dicyandiamide (DCD), a denitrification agent, altered GHG emissions. Manure and bedding were collected from feedlot pens and used to construct 3 piles (~1.9 m3 volume) each in winter (WI) and spring (SP). WI piles were turned once, and SP piles were turned twice. Methane, N2O, and NH3 emissions were collected. Methane and N2O flux measurements were collected from SP piles using a static chamber (3.7m L x 2.2m W x 0.9m H). Initial dry matter and nitrogen contents were 33.2 and 30.0% and 20.1 and 17.7 g/kg in WI and SP piles, respectively. Average ambient temperatures and wind speeds were 0.3oC and 10.7oC and 1.76 m/s and 1.97 m/s during WI and SP, respectively. Internal temperatures reached 51±3.9oC on d 4–11 and gradually decreased. Normalized CH4 averaged 2.19 mg٠s٠m-4 and N2O emissions averaged 0.84 mg٠s٠m-4, and were not different between the WI and SP piles. Turning did not affect CH4 emissions from WI piles, but were 55% greater (P < 0.05) when SP piles were turned a second time. Emissions of N2O increased 51% when WI and SP piles were turned (P < 0.05). Ammonia emissions were 83.5% greater from WI piles due to their higher initial concentrations of NH4+-N (2.21 vs. 1.11 g/kg; P < 0.05). Turning did not influence CH4 and N2O fluxes. Addition of DCD at pile formation appears to decrease N2O emissions and fluxes 3 and 10 d later. Turning management and season impacted overall CH4, N2O, and NH3 emissions. Fine-tuning manure handling and management during different seasons may effectively reduce GHG and NH3 emissions.