Impact of temporal upscaling and chemical transport model horizontal resolution on reducing ozone exposure misclassification

We have developed a Bayesian Maximum Entropy (BME) framework that integrates observations from a surface monitoring network and predictions from a Chemical Transport Model (CTM) to create improved exposure estimates that can be resolved into any spatial and temporal resolution. The flexibility of the framework allows for input of data in any choice of time scales and CTM predictions of any spatial resolution with varying associated degrees of estimation error and cost in terms of implementation and computation. This study quantifies the impact on exposure estimation error due to these choices by first comparing estimations errors when BME relied on ozone concentration data either as an hourly average, the daily maximum 8-h average (DM8A), or the daily 24-h average (D24A). Our analysis found that the use of DM8A and D24A data, although less computationally intensive, reduced estimation error more when compared to the use of hourly data. This was primarily due to the poorer CTM model performance in the hourly average predicted ozone. Our second analysis compared spatial variability and estimation errors when BME relied on CTM predictions with a grid cell resolution of 12 × 12 km2 versus a coarser resolution of 36 × 36 km2. Our analysis found that integrating the finer grid resolution CTM predictions not only reduced estimation error, but also increased the spatial variability in daily ozone estimates by 5 times. This improvement was due to the improved spatial gradients and model performance found in the finer resolved CTM simulation. The integration of observational and model predictions that is permitted in a BME framework continues to be a powerful approach for improving exposure estimates of ambient air pollution. The results of this analysis demonstrate the importance of also understanding model performance variability and its implications on exposure error

Modeled response of ozone to electricity generation emissions in the northeastern United States using three sensitivity techniques

ABSTRACT: Electrical generation units (EGUs) are important sources of nitrogen oxides (NOx) that contribute to ozone air pollution. A dynamic management system can anticipate high ozone and dispatch EGU generation on a daily basis to attempt to avoid violations, temporarily scaling back or shutting down EGUs that most influence the high ozone while compensating for that generation elsewhere. Here we investigate the contributions of NOx from individual EGUs to high daily ozone, with the goal of informing the design of a dynamic management system. In particular, we illustrate the use of three sensitivity techniques in air quality models—brute force, decoupled direct method (DDM), and higher-order DDM—to quantify the sensitivity of high ozone to NOx emissions from 80 individual EGUs. We model two episodes with high ozone in the region around Pittsburgh, PA, on August 4 and 13, 2005, showing that the contribution of 80 EGUs to 8-hr daily maximum ozone ranges from 1 to >5 ppb at particular locations. At these locations and on the two high ozone days, shutting down power plants roughly 1.5 days before the 8-hr ozone violation causes greater ozone reductions than 1 full day before; however, the benefits of shutting down roughly 2 days before the high ozone are modest compared with 1.5 days. Using DDM, we find that six EGUs are responsible for >65% of the total EGU ozone contribution at locations of interest; in some locations, a single EGU is responsible for most of the contribution. Considering ozone sensitivities for all 80 EGUs, DDM performs well compared with a brute-force simulation with a small normalized mean bias (–0.20), while this bias is reduced when using the higher-order DDM (–0.10). Implications: Dynamic management of electrical generation has the potential to meet daily ozone air quality standards at low cost. We show that dynamic management can be effective at reducing ozone, as EGU contributions are important and as the number of EGUs that contribute to high ozone in a given location is small (<6). For two high ozone days and seven geographic regions, EGUs would best be shut down or their production scaled back roughly 1.5 days before the forecasted exceedance. Including online sensitivity techniques in an air quality forecasting model can provide timely and useful information on which EGUs would be most beneficial to shut down or scale back temporarily

Wildfires in eastern Texas in August and September 2000: Emissions, aircraft measurements, and impact on photochemistry

The accuracy of wildfire air pollutant emission estimates was assessed by comparing observations of carbon monoxide (CO) and particulate matter (PM) concentrations in wildfire plumes to predictions of CO and PM concentrations, based on emission estimates and air quality models. The comparisons were done for observations made in southeast Texas in August and September of 2000. The fire emissions were estimated from acreage burned, fuel loading information, and fuel emission factor models. A total of 389 km2 (96,100 acres) burned in wildfires in the domain encompassing the Houston/Galveston-Beaumont/ Port Arthur (HGBPA) area during August and September 2000. On the days of highest wildfire activity, the fires resulted in an estimated 3700 tons of CO emissions, 250 tons of volatile organic carbon (VOC) emissions, 340 tons of PM2.5, and 50 tons of NOx emissions; estimated CO and VOC emissions from the fires exceeded light duty gasoline vehicle emissions in the Houston area on those days. When the appropriate aircraft data were available, aloft measurements of CO in the fire plumes were compared to concentrations of CO predicted using the emission estimates. Concentrations estimated based on emission predictions and air quality models were within a factor of 2 of the observed values. The estimated emissions from fires were used, together with a gridded photochemical model, to characterize the extent of dispersion of the fire emissions and the photochemistry associated with the fire emissions. Although the dispersion and photochemical impacts varied from fire to fire, for wildfires less than 10,000 acres, the greatest enhancements of CO and ozone concentrations due to the fire emissions were generally confined to regions within 10-100 km of the fire. Within 10 km of these fires, CO concentrations can exceed 2 ppm and ozone concentrations can be enhanced by 60 ppb. The extent of photo-oxidant formation in the plumes was limited by NOx availability and isoprene emissions from forested areas downwind of the fires provided most of the hydrocarbon reactivity in the plumes

Chemical Characteristics and Ozone Production in the Northern Colorado Front Range

We use the extensive set of aircraft and ground-based observations from the NSF/National Center for Atmospheric Research (NCAR) and State of Colorado Front Range Air Pollution and Photochemistry Éxperiment and the NASA DISCOVER-AQ experiments in summer 2014 together with the regional chemical transport model Weather Research and Forecast Model with Chemistry (WRF-Chem) to study the ozone production and chemical regimes in the Northern Colorado Front Range (NFR). We apply the model's Integrated Reaction Rate capability and chemical tendencies diagnostics and present results from an in-depth analysis of the ozone formation in various NFR regions for a case study of 12 August 2014. We further apply these diagnostics along a WRF online trajectory to assess the chemical evolution of an airmass during transport. The results show efficient ozone production within the NFR driven by the availability of NOx and an abundance of highly reactive volatile organic compound and also continued ozone production during the transport into the mountains. We identify CO, formaldehyde, higher alkanes, acetaldehyde, and isoprene among the volatile organic compound species with the highest efficiency in ozone production. Formaldehyde and acetaldehyde concentrations in the NFR have a significant contribution from photochemical production, which in turn is linked back to methane oxidation and to emissions of higher alkanes, isoprene, ethane, and propane. This study provides valuable policy information into the chemical fingerprint of surface ozone in the NFR, an area that is in nonattainment of the U.S. EPA ozone health standards and demonstrates the capability of the newly added diagnostic tool in WRF-Chem to address the drivers behind secondary production of pollutants in greater detail

Ambient measurements of monoterpenes near Cannabis cultivation facilities in Denver, Colorado

Colorado was one of the first US states to legalize the industrial-scale cultivation of Cannabis spp. for recreational purposes. In March 2018, there were 609 indoor Cannabis cultivation facilities (CCFs) in operation in Denver County with a recorded 550,000 mature plants (higher than 8 inches) under cultivation at any given time. It is known that cultivation of Cannabis spp. produces emissions of a group of highly reactive hydrocarbons, monoterpenes. There have been limited studies that have quantified mixing ratios of emitted monoterpenes in air outside CCFs. A field campaign was conducted in August 2016 in Denver County focused on six different CCF clusters near the intersection of interstate highways I-25 and I-70 during which a total of 150 ambient air samples were collected. Monoterpene mixing ratios near CCFs were ~408 ± 203 pptv; 4–8 times higher than samples collected from a “background” site located at the Denver City Park (75 ± 25 pptv). The composition of samples taken near CCFs were dominated by d-limonene (30%), β-myrcene (20%), and α-pinene (15%), which is similar to previously reported emission factors for Cannabis spp. Since β-myrcene was only detected in leaf enclosure studies, indoor CCF observations and ambient samples near CCFs and not detected at a background site, this particular compound could be used as a tracer for the Denver Cannabis production industry. The monoterpene speciation in ambient measurements varied across Denver suggesting differences in emissions between different Cannabis spp., or different growth stages. Given the observed variabilities in both composition and emission rates, it is critical for the accuracy of emissions inventories to develop strain-specific emission factors. This information, coupled with detailed information on each CCF, would greatly reduce the uncertainties currently present in monoterpene emission estimates for the Cannabis industry and their potential impact on air quality. © 2020 Elsevier Lt

Combining Bayesian methods and aircraft observations to constrain the HO. + NO2 reaction rate

Tropospheric ozone is the third strongest greenhouse gas, and has the highest uncertainty in radiative forcing of the top five greenhouse gases. Throughout the troposphere, ozone is produced by radical oxidation of nitrogen oxides (NOx = NO + NO2). In the upper troposphere (8–10 km), current chemical transport models under-estimate nitrogen dioxide (NO2) observations. Improvements to simulated NOx production from lightning have increased NO2 predictions, but the predictions in the upper troposphere remain biased low. The upper troposphere has low temperatures (T < 250 K) that increase the uncertainty of many important chemical reaction rates. This study constrains uncertain reaction rates by combining model predictions with measurements from the Intercontinental Chemical Transport Experiment-North America observational campaign. The results show that the nitric acid formation rate, which is the dominant sink of NO2 and radicals, is currently over-estimated by 22% in the upper troposphere. The results from this study suggest that the temperature sensitivity of nitric acid formation is lower than currently recommended. Since the formation of nitric acid removes nitrogen dioxide and radicals that drive the production of ozone, the revised reaction rate will affect ozone concentrations in upper troposphere impacting climate and air quality in the lower troposphere

Finely Resolved On-Road PM2.5 and Estimated Premature Mortality in Central North Carolina

To quantify the on-road PM2.5-related premature mortality at a national scale, previous approaches to estimate concentrations at a 12-km × 12-km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on-road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on-road PM2.5-related premature mortality in central North Carolina with two different concentration estimation approaches—(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36-km × 36-km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space–time interpolation technique to provide annual average PM2.5 concentrations at a Census-block level (∼105,000 Census blocks). The hybrid modeling approach estimated 24% more on-road PM2.5-related premature mortality than CMAQ. The major difference is from the primary on-road PM2.5 where the hybrid approach estimated 2.5 times more primary on-road PM2.5-related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on-road PM2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near-road primary PM2.5 and suggesting that previous studies may have underestimated premature mortality due to PM2.5 from traffic-related emissions

Particulate air pollutants, APOE alleles and their contributions to cognitive impairment in older women and to amyloidogenesis in experimental models

Exposure to particulate matter (PM) in the ambient air and its interactions with APOE alleles may contribute to the acceleration of brain aging and the pathogenesis of Alzheimer's disease (AD). Neurodegenerative effects of particulate air pollutants were examined in a US-wide cohort of older women from the Women's Health Initiative Memory Study (WHIMS) and in experimental mouse models. Residing in places with fine PM exceeding EPA standards increased the risks for global cognitive decline and all-cause dementia respectively by 81 and 92%, with stronger adverse effects in APOE ɛ4/4 carriers. Female EFAD transgenic mice (5xFAD+/−/human APOE ɛ3 or ɛ4+/+) with 225 h exposure to urban nanosized PM (nPM) over 15 weeks showed increased cerebral β-amyloid by thioflavin S for fibrillary amyloid and by immunocytochemistry for Aβ deposits, both exacerbated by APOE ɛ4. Moreover, nPM exposure increased Aβ oligomers, caused selective atrophy of hippocampal CA1 neurites, and decreased the glutamate GluR1 subunit. Wildtype C57BL/6 female mice also showed nPM-induced CA1 atrophy and GluR1 decrease. In vitro nPM exposure of neuroblastoma cells (N2a-APP/swe) increased the pro-amyloidogenic processing of the amyloid precursor protein (APP). We suggest that airborne PM exposure promotes pathological brain aging in older women, with potentially a greater impact in ɛ4 carriers. The underlying mechanisms may involve increased cerebral Aβ production and selective changes in hippocampal CA1 neurons and glutamate receptor subunits

Projecting wildfire emissions over the south-eastern United States to mid-century

Wildfires can impair human health because of the toxicity of emitted pollutants, and threaten communities, structures and the integrity of ecosystems sensitive to disturbance. Climate and socioeconomic factors (e.g. population and income growth) are known regional drivers of wildfires. Reflecting changes in these factors in wildfire emissions estimates is thus a critical need in air quality and health risk assessments in the south-eastern United States. We developed such a methodology leveraging published statistical models of annual area burned (AAB) over the US Southeast for 2011-2060, based on county-level socioeconomic and climate projections, to estimate daily wildfire emissions in selected historical and future years. Projected AABs were 7 to 150% lower on average than the historical mean AABs for 1992-2010; projected wildfire fine-particulate emissions were 13 to 62% lower than those based on historical AABs, with a temporal variability driven by the climate system. The greatest differences were in areas of large wildfire impacts from socioeconomic factors, suggesting that historically based (static) wildfire inventories cannot properly represent future air quality responses to changes in these factors. The results also underscore the need to correct biases in the dynamical downscaling of wildfire climate drivers to project the health risks of wildfire emissions more reliably

Evaluating wildfire emissions projection methods in comparisons of simulated and observed air quality

Climate warming has been implicated as a major driver of recent catastrophic wildfires worldwide but analyses of regional differences in US wildfires show that socioeconomic factors also play a large role. We previously leveraged statistical projections of annual areas burned (AAB) over the fast-growing southeastern US that include both climate and socioeconomic changes from 2011 to 2060 and developed wildfire emissions estimates over the region at 12 km × 12 km resolution to enable air quality (AQ) impact assessments for 2010 and selected future years. These estimates employed two AAB datasets, one using statistical downscaling ("statistical d-s") and another using dynamical downscaling ("dynamical d-s") of climate inputs from the same climate realization. This paper evaluates these wildfire emissions estimates against the U.S. National Emissions Inventory (NEI) as a benchmark in contemporary (2010) simulations with the Community Multiscale Air Quality (CMAQ) model and against network observations for ozone and particulate matter below 2.5 μ m in diameter (PM2:5). We hypothesize that our emissions estimates will yield model results that meet acceptable performance criteria and are comparable to those using the NEI. The three simulations, which differ only in wildfire emissions, compare closely, with differences in ozone and PM2:5 below 1 % and 8 %, respectively, but have much larger maximum mean fractional biases (MFBs) against observations (25 % and 51 %, respectively). The largest biases for ozone are in the fire-free winter, indicating that modeling uncertainties other than wildfire emissions are mainly responsible. Statistical d-s, with the largest AAB domain-wide, is 7 % more positively biased and 4 % less negatively biased in PM2:5 on average than the other two cases, while dynamical d-s and the NEI differ only by 2 %-3 % partly because of their equally large summertime PM2:5 underpredictions. Primary species (elemental carbon and ammonium from ammonia) have good-to-acceptable results, especially for the downscaling cases, providing confidence in our emissions estimation methodology. Compensating biases in sulfate (positive) and in organic carbon and dust (negative) lead to acceptable PM2:5 performance to varying degrees (MFB between -14 % and 51 %) in all simulations. As these species are driven by secondary chemistry or nonwildfire sources, their production pathways can be fruitful avenues for CMAQ improvements. Overall, the downscaling methods match and sometimes exceed the NEI in simulating current wildfire AQ impacts, while enabling such assessments much farther into the future