Quantifying atmospheric loadings of total phosphorus (TP) to freshwater environments is essential to improve understanding of its fate and transport, and to mitigate the effects of excessive levels in freshwater ecosystems. To date, atmospheric deposition of TP in the U.S. is poorly characterized due to the lack of long-term deposition observations. Here, we integrate several historical datasets to develop an estimate of dry and wet deposition to the Great Lakes region. For dry deposition, we use TP concentrations in fine particulate matter (PM2.5) samples from fourteen land-based IMPROVE sites (2013-2020) upwind of the Great Lakes to provide new fine particle phosphorus dry deposition estimates. For wet deposition, we use TP concentrations in wet-only precipitation samples collected at eleven land-based sites (2001-2009) in the Great Lakes region. For both wet and dry deposition, a seasonal cycle is evident with higher concentrations in warmer and wetter months when compared to colder months. Additionally, there is an increasing gradient from north to south in wet deposition, likely driven by both higher precipitation and increased emissions near southern sites. Despite different sampling time periods, these updated observations can provide further constraints on the TP loadings to each of the five Great Lakes. We estimate annual deposition of TP to Lakes Superior, Michigan, Huron, Erie and Ontario at 526, 702, 495, 212, and 185 MTA per year, which is lower than prior estimates for Lakes Superior, Erie and Ontario, comparable for Lake Huron, and about two times greater for Lake Michigan. When considering only the contribution of fine particulate PM to the dry deposition, wet deposition dominated over dry at all lakes except for Lake Huron. However, prior global estimates suggest greater contributions from larger particles (PM10 and PM100), yet observations to validate these estimates over the Great Lakes are not available. Our findings indicate that dry deposition of a range of particle sizes are needed to constrain the total atmospheric deposition of TP over the Great Lakes.
Animal production has the potential to emit various atmospheric pollutants including ammonia (NH3), which can impact human health, atmospheric visibility and ecosystem health through gaseous NH3 and associated NH 4 + particulate matter deposition. Emission estimating methodologies were developed using statistical models to estimate daily NH3 emissions from swine grow-finish barns based on National Air Emissions Monitoring Study (NAEMS) data. Models were developed with variables that represented production, manure management and environmental conditions. Model performance was evaluated for predicting NAEMS and non-NAEMS emissions, consistency of model coefficients and sensitivity to different model input values. Accounting for ease of variable measurement, the best performing models for predicting NAEMS emissions were models 1b and 16a, both of which accounted for the influence of temperature, swine inventory and weight, but used different predictor and response variables. In predicting NAEMS emissions, model 1b had mean error (ME) and mean bias (MB) values of 1.6 kg day-1 (normalized mean error (NME) = 25.9 %) and 0.1 kg day-1 (normalized mean bias (NMB) = 1.2 %), respectively, which were slightly lower than the corresponding values for model 16a (ME/NME = 1.8 kg day-1/25.9 % and MB/NMB = 0.4 kg day-1/5.8 %). Model 1b performed better in predicting non-NAEMS emissions, but model 16a had more reasonable sensitivity when barn live animal weight was >215,000 kg. Models using nitrogen feed intake as a predictor variable also performed well in predicting emissions and although these models have greater uncertainty due to limited NAEMS measurements, they could potentially account for changes in feed practices.
Pollen grains may contain allergens that exacerbate allergic respiratory diseases like asthma and rhinitis. In the presence of water, pollen grains (10-100 μm) can rupture to produce sub-pollen particles (SPP) with diameters <2.5 μm, which in comparison to intact pollen grains, have longer atmospheric lifetimes and greater penetration to the lower lung. The current study examines SPP, fungal spores, and bacteria in size-resolved atmospheric particulate matter (PM) using chemical and biological tracers. During springtime tree pollen season in Iowa City, Iowa, fine particle (PM2.5) concentrations of fructose (a pollen chemical tracer) increased on rainy sampling periods, especially during severe thunderstorms, and peaked when a tornado struck nearby. Submicron fluorescent particles, measured by single-particle fluorescence spectroscopy, were also enhanced during rain events, particularly thunderstorms in agreement with the chemical tracer measurements. PM2.5 sucrose (a pollen chemical tracer) concentrations were higher in early spring when nighttime temperatures were closer to freezing, while fructose concentrations were higher in late spring with warmer temperatures, consistent with chemical tracers being sensitive to seasonal temperature influences. The first co-located measurements of fructose and Bet v 1 (birch pollen allergen), indicated that SPP ranged in diameter from <0.25 to 2.5 μm during rainy sampling periods and that allergens and carbohydrates exhibited distinct size distributions. Meanwhile, mannitol (a fungal spore tracer) peaked on warm, dry days following rain and was primarily in supermicron particles (>1.0 μm), which is consistent with intact fungal spore diameters (1-30 μm). Bacterial endotoxins in PM also increased during extreme weather events, primarily in supermicron particles. While the concentrations of fructose, mannitol, and endotoxin all increased in PM2.5 μm during thunderstorms, the greatest relative increase in concentration was observed for fructose. Together, these observations suggest that SPP containing starch granules and allergens (Bet v 1) were released during rainy sampling periods. This study advances the use of chemical tracers to track SPP and other bioaerosols in the atmosphere, by providing new insight to their size distribution and response to extreme weather conditions.
Air pollution is a major contributor to mortality and adverse health outcomes, particularly in pregnant women and children, with vehicular emissions as a key source. This study aimed to characterize long-term exposure to fine particulate matter (PM2·5), evaluate potential health risks, and quantify the contribution of road transport and proximity to roads in The Gambia, Kenya, and Mozambique. Multi-temporal personal exposure data were interpolated into continuous spatial surfaces and aggregated into daily averages, which were then compared with World Health Organization (WHO) guidelines to assess potential health impacts. Multi-distance buffers at 10 m intervals up to 1000m from major roads were created to examine pollutant variation with road proximity. Overlay analyses and linear regression models were applied to quantify the road-edge effect on pollution exposure. The Inverse Distance Weighting interpolation technique produced accurate PM2·5 field in all sites (R2>0.55, MAE<4 μg/m3 and RMSE<10 μg/m3) based on leave-one-out cross-validation. PM2·5 concentrations were highest in The Gambia across all land use and land cover (LULC) types, reflecting localized industrial and vehicular emissions. Elevated pollutant levels were observed in herbaceous wetlands, bare land, and built-up areas. Most sites across all three countries exceeded WHO recommended thresholds, indicating significant health risks. PM2·5 decreased with distance from roads in Rabai (Kenya) and Mozambique, while in other sites it increased away from roads. These findings highlight the combined influence of multiple local sources, including traffic, fire smoke, wetlands, and industry, on pollutant exposure. The study demonstrates the utility of WHO thresholds for health risk assessment and shows how road proximity affects pollutant dispersal. The results provide critical insights for modeling air pollution exposure and informing mitigation strategies in sub-Saharan Africa.
In cystic fibrosis (CF) lung disease, increased exposure to PM2.5 (particulate matter <2.5 μm in diameter) has been linked to more frequent pulmonary exacerbations, yet the timing of exposure that presents the greatest exacerbation risk remains uncertain. Our study investigated whether short-term increases in ambient fine particulate matter (PM2.5) contribute to exacerbation risk in individuals with CF. We analyzed data from 108 participants across 17 U.S. clinical sites enrolled in the Early Intervention in Cystic Fibrosis Exacerbation study (October 2011-July 2015), who recorded forced expiratory volume in 1 s (FEV1) measurements at least twice weekly. Exacerbation cases were identified via the FEV1 indicated exacerbation signal (FIES), defined as a ≥10 % predicted decline in FEV1. Daily 24-h average PM2.5 concentrations were estimated at the residential ZIP code using a high-resolution spatiotemporal exposure model. A time-stratified case-crossover design modeled exacerbation onset and PM2.5 exposure, controlled for temperature, relative humidity, and holiday impacts. We analyzed 6936 FEV1 observations yielding 1432 exacerbation cases, averaging 7.7 cases per person-year, matched to 4794 control dates in the same year, month, and day of the week. Increased exacerbation risk occurred one (OR: 1.18; 95 % CI: 1.02-1.36), two (OR: 1.20; 95 % CI: 1.04-1.40), and three (OR: 1.23; 95 % CI: 1.05-1.43) days after PM2.5 exposure, but not on the day of onset (OR: 1.07; 95 % CI: 0.92-1.25). Integrating home-monitored lung function with hyperlocal exposure estimates provides improved temporal resolution for detecting acute environmental triggers and may inform early intervention strategies in CF care. e-ICE clinical trial registered with clinicaltrials.gov/ (NCT01104402).
Positive matrix factorization (PMF) can be used to develop more targeted air quality mitigation strategies by identifying major sources of a pollutant in an area. This technique is dependent, however, on the ability of PMF to resolve factors that accurately represent all sources of that pollutant in an area. We investigated how the accuracy of PMF solutions might be influenced by monitoring data characteristics, such as temporal resolution, monitoring location, and species composition, to better inform the use of PMF in VOC mitigation strategies. We applied PMF to five VOC monitoring programs collected within a four-year period in Colorado and found generally consistent factors, which we identified as oil extraction, processing, and evaporation; natural gas; vehicle exhaust; and liquid gasoline/short-lived oil and gas. The main determinant influencing whether or not a dataset resolved each of these sources was whether the dataset had a comprehensive list of VOC species covering key species of each source. Pollution spikes were not well-modeled in any of the solutions. Hyperlocal and volatile chemical product factors expected to be resolved in the industrialized, urban location were also missing, highlighting three limitations of PMF analysis. Wind direction dependence and diurnal trends aided in source identification, suggesting that high-time resolution data is important for developing actionable PMF results. Based on these findings, we recommend that air monitoring for PMF-informed VOC mitigation efforts include high temporal resolution and a comprehensive array of VOC species.
Several areas in the Lake Michigan region are violating the human health-based ozone (O3) National Ambient Air Quality Standard. Land-water meteorology driven build-up of precursor pollutants (NOX and VOC) from mobile and stationary sources undergo photochemical O3 production and result in seasonal high O3 episodes during the spring and summertime. Routine and specialized surface measurements coupled with airborne and remotely sensed measurements from the 2017 Lake Michigan Ozone Study (LMOS) provide an opportunity to evaluate photochemical grid model representation of these processes. The Community Multiscale Air Quality Model (CMAQ) was applied at a 4-km grid resolution for the Lake Michigan region and compared against routine and field study measurements to determine how well the modeling system captures emissions, meteorology, and chemical production during O3 episodes. The model captured day to day and diurnal variability in observed O3 along Lake Michigan but often missed peak O3. Surface level NO2 was generally well characterized, but the model seemed to be missing a significant source of VOC, likely both regional and local (not necessarily the same source sector) based on model sensitivities. On June 2, 2017, the model captured the timing of the lake to land breeze but underestimated near-surface wind speed which coincided with peak O3. The underestimation of surface O3 north of Chicago at Zion, Illinois, on this afternoon may be related to over-water vertical mixing, the modeled lake breeze being too weak to transport ozone and precursors back onshore, regional and aloft underprediction of O3, or some combination of each of these factors. Despite underpredictions of peak O3 at some monitors on certain days, the modeling system is generally useful for developing control scenarios for this region. Model-predicted O3 sensitivity to precursors matched other assessments and suggests both NOX and VOC anthropogenic emissions are important to reduce O3 in the Chicago area.
Wildland fires, which includes both wild and prescribed fires, and agricultural fires in sum are one of the largest sources of fine particulate matter (PM2.5) emissions to the atmosphere in the United States (US). Although wildland fire PM2.5 emissions are primarily composed of carbonaceous material, many other elements including trace metals are emitted at very low levels. Lead (Pb) is a US Environmental Protection Agency (EPA) criteria pollutant that is ubiquitous in the environment at very low concentrations including in biomass that can burn and emit Pb into the atmosphere. Although fires may emit Pb at very low concentrations, they can be a source of sizeable Pb emissions to the atmosphere because of the large quantity of PM2.5 emitted from fires. In this work, we measure Pb concentrations in unburned biomass, ash/residues, and particulate matter <2.5 μm (PM2.5) emitted from wildland fires using in-field measurements near prescribed fires and in laboratory simulations. Emission factors were calculated for multiple biomass types, representative of different regions of the US including grasslands in Oregon and Kansas; forest litter from Oregon, Montana, Minnesota, and North Carolina; and peat cores from Minnesota. Most of the biomass Pb remains in the ash/residues. The small percentage (<10%) that is emitted in PM2.5 is dependent on the biomass Pb concentration. The emissions factors measured here are several orders of magnitude lower than some reported in the literature, but the studies exhibited a wide range of values, which may be due to large uncertainties in the measurement method rather than differences in Pb emissions. Wildland fires are expected to increase in size and frequency in future years and these new emission factors can be used to improve the accuracy of Pb emissions estimates and better constrain our understanding of Pb emissions to the atmosphere.
Nanofibrous filter materials were prepared by electrospinning a solution of 28 wt% poly(vinylidene fluoride) in N,N-dimethylacetamide with and without the addition of 2 wt% AgNO3, Cu(NO3)2·2.5H2O or ZnCl2. X-ray diffraction, scanning electron microscopy with energy dispersive X-ray spectroscopy, inductively coupled plasma mass spectroscopy, thermogravimetric analysis, contact angle measurement, nitrogen sorption, and mercury intrusion porosimetry methods were used for the characterization of physical structure as well as the chemical composition of the electrospun materials. Particle filtration efficiency and antiviral activity against the SARS-CoV-2 alpha variant were tested in order to estimate the suitability of the prepared electrospun filter materials for application as indoor air filtration systems with virucidal properties. All filter materials prepared with salts demonstrated very high particle filtration efficiency (≥98.0%). The best antiviral activity was demonstrated by a material containing Cu(NO3)2·2.5H2O in the spinning solution, which displayed the decrease in the number of infectious virions by three orders of magnitude after a contact time of 12 h. Materials with the addition of AgNO3 and ZnCl2 decreased the number of infectious virions after the same contact time by only ∼8 and ∼11 times, respectively.
Tracer flux ratio (TFR) methodology performed downwind of 15 active oil and natural gas production sites in Ohio County, West Virginia sought to quantify air pollutant emissions over two weeks in April 2018. In coordination with a production company, sites were randomly selected depending on wind forecasts and nearby road access. Methane (CH4), ethane (C2H6), and tracer gas compounds (acetylene and nitrous oxide) were measured via tunable infrared direct absorption spectroscopy. Ion signals attributed to benzene (C6H6) and other volatile gases (e.g., C7 - C9 aromatics) were measured via proton-transfer reaction time-of-flight mass spectrometry. Short-term whole facility emission rates for 12 sites are reported. Results from TFR were systematically higher than the sum of concurrent on-site full flow sampler measurements, though not all sources were assessed on-site in most cases. In downwind plumes, the mode of the C2H6:CH4 molar ratio distribution for all sites was 0.2, which agreed with spot sample analysis from the site operator. Distribution of C6H6:CH4 ratios was skew but values between 1 and 5 pptv ppbv-1 were common. Additionally, the aromatic profile has been attributed to condensate storage tank emissions. Average ratios of C7 - C9 to C6H6 were similar to other literature values reported for natural gas wells.
Ethylene oxide (EtO) is a hazardous air pollutant that can be emitted from a variety of difficult to measure industrial sources, such as fugitive leaks, wastewater handling, and episodic releases. Emerging next generation emission measurement (NGEM) approaches capable of time-resolved, low parts per billion by volume (ppbv) method detection limits (MDLs) can help facilities understand and reduce EtO and other air pollutant emissions from these sources yielding a range of environmental and public health benefits. In October 2021, a first of its kind 4-day observational study was conducted at an EtO chemical facility in the midwestern United States. The study had dual objectives to both improve understanding of EtO emission sources within the facility and advance NGEM methods. Using cavity ring-down spectroscopy (CRDS) instruments, a combination of mobile surveys and stationary multipoint process unit monitoring assessed EtO concentrations in and near facility operations, while testing and comparing measurement methods. The study concluded that four main areas of EtO source emissions existed within the facility, each possessing unique emission characteristics. Episodic EtO emissions from supply railcar switchovers and batch reactor washouts, lasting seconds to minutes in duration, produced EtO concentrations exceeding 500 ppbv inside the process unit in some cases. In one instance, EtO at ~30 ppbv was briefly observed hundreds of meters from the process unit. Lower level but more sustained EtO concentrations were observed near an EtO transfer pump and wastewater tank outfall and drain system. Overall, 4.6% of mobile survey data were above the 1.2 ppbv mobile test MDL while the nine stationary sampling locations ranged from 17.7% to 82.8% of data above the 1.0 ppbv multipoint test MDL. This paper describes the EtO emissions observed in and near the four defined source areas within the facility and provides details of the NGEM method development advances accomplished as part of the study.
In April of 2018, an optical gas imaging (OGI) and full flow sampler (FFS) emissions measurement study of pneumatic controllers (PCs) was conducted at 15 oil and natural gas production sites in West Virginia. The objective of the study was to identify and characterize PC systems with excessive emissions caused by maintenance issues or nonoptimized process conditions. A total of 391 PC systems were found on the sites and all were classified by the operator as snap-acting (on/off) intermittent venting PCs (IPCs) that should exhibit little gas release while the PC is closed between actuation events. The population was comprised of two groups, 259 infrequently actuating, lower emitting (LE) IPCs and 132 gas processing unit (GPU) liquid level IPCs and associated dump valve actuators that vent more frequently and have larger emission volumes. Using a PC-specific OGI inspection protocol with an assumed whole gas OGI detection threshold of 2.0 scfh, only 2 out of 259 LE-IPCs exhibited OGI detectable emissions indicating good inspection and maintenance practices for this category. Due to combined (ganged) GPU exhaust vents, the OGI inspection of the GPU liquid level IPCs was comparatively less informative and determination of single component IPC emissions by the FFS was more difficult. The time resolved FFS measurements of GPU IPCs defined three categories of operation: one that indicated proper function and two associated with higher emissions that may result from an IPC maintenance or process issues. The overall GPU IPC emission distribution was heavy tailed, with a median value of 12.8 scfh, similar to the 13.5 scfh whole gas IPC emission factor (EF). Total emissions were dominated by non-optimal temporal profile high-emitter IPC cases with the top 20% of IPC systems accounting for between 51.3% and 70.7% of GPU liquid level IPC emissions by volume. The uncertainty in the estimate was due to the ganged nature of the GPU exhaust vents. The highest GPU IPC emission came from a single malfunctioning unit with a measured whole gas value of 157 scfh. Up to six IPCs exceeded 100 scfh. An analysis of FFS emission measurements compared to liquids production per IPC unit employed indicated that production sites operating at a high level of liquids production test the limits of the site engineering, likely resulting in higher IPC emissions. Overall, this study found that the LE-IPCs with OGI-verified low closed bleed rates may emit well below the IPC EF while GPU liquid level IPC systems are likely well represented by the current IPC EF. IPCs that are experiencing a maintenance or process issue or that are operating at sites with a very high product throughput per IPC employed can emit at rates exceeding ten times IPC EF.
Ventilation is of primary concern for maintaining healthy indoor air quality and reducing the spread of airborne infectious disease, including COVID-19. In addition to building-level guidelines, increased attention is being placed on room-level ventilation. However, for many universities and schools, ventilation data on a room-by-room basis are not available for classrooms and other key spaces. We present an overview of approaches for measuring ventilation along with their advantages and disadvantages. We also present data from recent case studies for a variety of institutions across the United States, with various building ages, types, locations, and climates, highlighting their commonalities and differences, and examples of the use of this data to support decision making.
A measurement campaign characterized methane and other emissions from 15 natural gas production sites. Sites were surveyed using optical gas imaging (OGI) cameras to identify fugitive and vented emissions, with the methane mass emission rate quantified using a full flow sampler. We present storage tank emissions in context of all site emissions, followed by a detailed account of the former. In total, 224 well pad emission sources at 15 sites were quantified yielding a total emission rate of 57.5 ± 2.89 kg/hr for all sites. Site specific emissions ranged from 0.4 to 10.5 kg/hr with arithmetic and geometric means of 3.8 and 2.2 kg/hr, respectively. The two largest categories of emissions by mass were pneumatic devices (35 kg/hr or ~61% of total) and tanks (14.3 kg/hr or ~25% of total). Produced water and condensate tanks at all sites employed emissions control devices. Nevertheless, tanks may still lose gas via component leaks as observed in this study. The total number of tanks at all sites was 153. One site experienced a major malfunction and direct tank measurements were not conducted due to safety concerns and may have represented a super-emitter as found in other studies. The remaining sites had 143 tanks, which accounted for 42 emissions sources. Leaks on controlled tanks were associated with ERVs, PRVs, and thief hatches. Since measurements represented snapshots-in-time and could only be compared with modeled tank emission data, it was difficult to assess real capture efficiencies accurately. Our estimates suggest that capture efficiency ranged from 63 to 92% for controlled tanks.
Wildland fires are a major source of gases and aerosols, and the production, dispersion, and transformation of fire emissions have significant ambient air quality impacts and climate interactions. The increase in wildfire area burned and severity across the United States and Canada in recent decades has led to increased interest in expanding the use of prescribed fires as a forest management tool. While the primary goal of prescribed fire use is to limit the loss of life and property and ecosystem damage by constraining the growth and severity of future wildfires, a potential additional benefit of prescribed fire - reduction in the adverse impacts of smoke production and greenhouse gas (GHG) emissions - has recently gained the interest of land management agencies and policy makers in the United States and other nations. The evaluation of prescribed fire/wildfire scenarios and the potential mitigation of adverse impacts on air quality and GHGs requires fuel layer specific pollutant emission factors (EFs) for fire prone forest ecosystems. Our study addresses this need with laboratory experiments measuring EFs for carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), ethyne (C2H2), formaldehyde (H2CO), formic acid (CH2O2), hydrogen cyanide (HCN), fine particulate matter (PM2.5), nitric oxide (NO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and total reduced sulfur (TRS) for the burning of individual fuel components from three forest ecosystems which account for a large share of wildfire burned area and emissions in the western United States and Canada - Douglas fir, ponderosa pine, and black spruce/jack pine.
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The formation of secondary organic aerosol (SOA) indoors is one of the many consequences of the rich and complex chemistry that occurs therein. Given particulate matter has well documented health effects, we need to understand the mechanism for SOA formation indoors and its resulting composition. This study evaluates some uncertainties that exist in quantifying gas-to-particle partitioning of SOA-forming compounds using an indoor detailed chemical model. In particular, we investigate the impacts of using different methods to estimate compound vapour pressures as well as simulating the formation of highly oxygenated organic molecules (HOM) via auto-oxidation on SOA formation indoors. Estimation of vapour pressures for 136 α-pinene oxidation species by six investigated methods led to standard deviations of 28-216%. Inclusion of HOM formation improved model performance across three of the six assessed vapour pressure estimation methods when comparing against experimental data, particularly when the NO2 concentration was relatively high. We also explored the predicted SOA composition using two product classification methods, the first assuming the molecule is dominated by one functionality according to its name, and the second accounting for the fractional weighting of each functional group within a molecule. The SOA composition was dominated by the HOM species when the NO2-to-α-terpineol ratio was high for both product classification methods, as these conditions promoted formation of the nitrate radical and hence formation of HOM monomers. As the NO2-to-α-terpineol ratio decreased, peroxides and acids dominated the simple classification, whereas for the fractional classification, carbonyl and alcohol groups became more important.
Due to the global response to the COVID-19 pandemic, there have been a variety of policy responses that have produced a range of expected and unexpected effects on society and our surrounding environment. One widely reported result of the pandemic response is that travel restrictions have resulted in improvements in regional air quality. This study aims to determine the effect of COVID-19 related Stay at Home precautions on air quality in a metropolitan area. We specifically focus on CO, NO2, and PM10 in Maricopa County (Phoenix), Arizona, as these all contribute to local air quality concerns. The role of meteorological parameters on ambient concentrations for these pollutants was investigated by using the local planetary boundary layer height (PBH) to account for vertical mixing. Across all three sites studied, there was no uniform decrease in either CO or NO2, even when freeway traffic volume was down by ~35%. For PM10, there was a significant decrease of ~45% seen at all the sites for the period most directly impacted by local Stay at Home restrictions compared to the past two years. This indicates that different pollutants have fundamentally different behavior in the local environment and suggests that these pollutants originate from different sources.
PM2.5 concentration fields that correspond to just meeting national ambient air quality standards (NAAQS) are useful for characterizing exposure in regulatory assessments. Computationally efficient methods that incorporate predictions from photochemical grid models (PGM) are needed to realistically project baseline concentration fields for these assessments. Thorough cross validation (CV) of hybrid spatial prediction models is also needed to better assess their predictive capability in sparsely monitored areas. In this study, a system for generating, evaluating, and projecting PM2.5 spatial fields to correspond with just meeting the PM2.5 NAAQS is developed and demonstrated. Results of ten-fold CV based on standard and spatial cluster withholding approaches indicate that performance of three spatial prediction models improves with decreasing distance to the nearest neighboring monitor, improved PGM performance, and increasing distance from sources of PM2.5 heterogeneity (e.g., complex terrain and fire). An air quality projection tool developed here is demonstrated to be effective for quickly projecting PM2.5 spatial fields to just meet NAAQS using realistic spatial response patterns based on air quality modeling. PM2.5 tends to be most responsive to primary PM2.5 emissions in urban areas, whereas response patterns are relatively smooth for NOx and SO2 emission changes. On average, PM2.5 is more responsive to changes in anthropogenic primary PM2.5 emissions than NOx and SO2 emissions in the contiguous U.S.
Urban air pollution is a substantial threat to human health. Traffic emissions remain a large contributor to air pollution in urban areas. The mobility restrictions put in place in response to the COVID-19 pandemic provided a large-scale real-world experiment that allows for the evaluation of changes in traffic emissions and the corresponding changes in air quality. Here we use observational data, as well as modelling, to analyse changes in nitrogen dioxide, ozone, and particulate matter resulting from the COVID-19 restrictions at the height of the lockdown period in Spring of 2020. Accounting for the influence of meteorology on air quality, we found that reduction of ca. 30-50 % in traffic counts, dominated by changes in passenger cars, corresponded to reductions in median observed nitrogen dioxide concentrations of ca. 40 % (traffic and urban background locations) and a ca. 22 % increase in ozone (urban background locations) during weekdays. Lesser reductions in nitrogen dioxide concentrations were observed at urban background stations at weekends, and no change in ozone was observed. The modelled reductions in median nitrogen dioxide at urban background locations were smaller than the observed reductions and the change was not significant. The model results showed no significant change in ozone on weekdays or weekends. The lack of a simulated weekday/weekend effect is consistent with previous work suggesting that NOx emissions from traffic could be significantly underestimated in European cities by models. These results indicate the potential for improvements in air quality due to policies for reducing traffic, along with the scale of reductions that would be needed to result in meaningful changes in air quality if a transition to sustainable mobility is to be seriously considered. They also confirm once more the highly relevant role of traffic for air quality in urban areas.