Deep learning bias correction of GEMS tropospheric NO2: A comparative validation of NO2 from GEMS and TROPOMI using Pandora observations.

Despite advancements in satellite instruments, such as those in geostationary orbit, biases continue to affect the accuracy of satellite data. This research pioneers the use of a deep convolutional neural network to correct bias in tropospheric column density of NO2 (TCDNO2) from the Geostationary Environment Monitoring Spectrometer (GEMS) during 2021-2023. Initially, we validate GEMS TCDNO2 against Pandora observations and compare its accuracy with measurements from the TROPOspheric Monitoring Instrument (TROPOMI). GEMS displays acceptable accuracy in TCDNO2 measurements, with a correlation coefficient (R) of 0.68, an index of agreement (IOA) of 0.79, and a mean absolute bias (MAB) of 5.73321 × 1015 molecules/cm2, though it is not highly accurate. The evaluation showcases moderate to high accuracy of GEMS TCDNO2 across all Pandora stations, with R values spanning from 0.46 to 0.80. Comparing TCDNO2 from GEMS and TROPOMI at TROPOMI overpass time shows satisfactory performance of GEMS TCDNO2 measurements, achieving R, IOA, and MAB values of 0.71, 0.78, and 6.82182 × 1015 molecules/cm2, respectively. However, these figures are overshadowed by TROPOMI's superior accuracy, which reports R, IOA, and MAB values of 0.81, 0.89, and 3.26769 × 1015 molecules/cm2, respectively. While GEMS overestimates TCDNO2 by 52 % at TROPOMI overpass time, TROPOMI underestimates it by 9 %. The deep learning bias corrected GEMS TCDNO2 (GEMS-DL) demonstrates a marked enhancement in the accuracy of original GEMS TCDNO2 measurements. The GEMS-DL product improves R from 0.68 to 0.88, IOA from 0.79 to 0.93, MAB from 5.73321 × 1015 to 2.67659 × 1015 molecules/cm2, and reduces MAB percentage (MABP) from 64 % to 30 %. This represents a significant reduction in bias, exceeding 50 %. Although the original GEMS product overestimates TCDNO2 by 28 %, the GEMS-DL product remarkably minimizes this error, underestimating TCDNO2 by a mere 1 %. Spatial cross-validation across Pandora stations shows a significant reduction in MABP, from a range of 45 %-105.6 % in original GEMS data to 24 %-59 % in GEMS-DL.

Innovative approaches for accurate ozone prediction and health risk analysis in South Korea: The combined effectiveness of deep learning and AirQ.

Short-term exposure to ground-level ozone (O3) poses significant health risks, particularly respiratory and cardiovascular diseases, and mortality. This study addresses the pressing need for accurate O3 forecasting to mitigate these risks, focusing on South Korea. We introduce Deep Bias Correction (Deep-BC), a novel framework leveraging Convolutional Neural Networks (CNNs), to refine hourly O3 forecasts from the Community Multiscale Air Quality (CMAQ) model. Our approach involves training Deep-BC using data from 2016 to 2019, including CMAQ's 72-hour O3 forecasts, 31 meteorological variables from the Weather Research and Forecasting (WRF) model, and previous days' station measurements of 6 air pollutants. Deep-BC significantly outperforms CMAQ in 2021, reducing biases in O3 forecasts. Furthermore, we utilize Deep-BC's daily maximum 8-hour average O3 (MDA8 O3) forecasts as input for the AirQ+ model to assess O3's potential impact on mortality across seven major provinces of South Korea: Seoul, Busan, Daegu, Incheon, Daejeon, Ulsan, and Sejong. Short-term O3 exposure is associated with 0.40 % to 0.48 % of natural cause and respiratory deaths and 0.67 % to 0.81 % of cardiovascular deaths. Gender-specific analysis reveals higher mortality rates among men, particularly from respiratory causes. Our findings underscore the critical need for region-specific interventions to address air pollution's detrimental effects on public health in South Korea. By providing improved O3 predictions and quantifying its impact on mortality, this research offers valuable insights for formulating targeted strategies to mitigate air pollution's adverse effects. Moreover, we highlight the urgency of proactive measures in health policies, emphasizing the significance of accurate forecasting and effective interventions to safeguard public health from the deleterious effects of air pollution.

A case study of surface ozone source contributions in the Seoul metropolitan area using the adjoint of CMAQ.

To quantitatively investigate the transboundary behaviors and source attributions of ozone (O3) and its precursor species over East Asia, we utilize the adjoint technique in the CMAQ modeling system (the CMAQ adjoint). Our focus is on the Seoul Metropolitan Area (SMA) in South Korea, which is the receptor region of this study. We examine the contributions of both local and transported emissions to an O3 exceedance episode observed on June 3, 2019, estimating up to four days in advance. By using the CMAQ adjoint, we can determine the sensitivity of O3 remaining in the SMA to changes in O3 precursor emissions (emissions-based sensitivity) and concentrations (concentrations-based sensitivity) along the long-range transport pathways and emission source regions overseas. These include Beijing-Tianjin-Hebei (BTH), Shandong, Yangtze River Delta (YRD), and Central China. CMAQ adjoint-derived source attributions suggest that overseas precursor emissions and O3 contributed significantly to the O3 exceedance event in SMA. The emissions-based sensitivities revealed that precursor emissions originating from Shandong, YRD, Central China, and BTH contributed 11.42 ppb, 4.28 ppb, 1.24 ppb, 0.9 ppb, respectively, to the O3 exceedance episode observed in the SMA. Meanwhile, Korean emissions contributed 31.1 ppb. Concentrations-based sensitivities indicated that 19.3 ppb of contributions originated in regions beyond eastern China and directly affected the O3 level in the SMA in the form of background O3. In addition to capturing the transboundary movements of air parcels between the source and receptor regions, we performed HYSPLIT backward trajectory analyses. The results align with the trajectories of O3 and its precursors that we obtained from the adjoint method. This study represents a unique effort in employing the adjoint technique to examine the impacts of regional O3 on South Korea, utilizing a combination of emissions-based and concentrations-based sensitivities.Implications: This research brings to light the critical role of both local and regional precursor emissions in contributing to an ozone (O3) exceedance event in the Seoul Metropolitan Area (SMA), South Korea. Utilizing the CMAQ adjoint technique, a novel approach in the context of South Korea's O3 investigations, we were able to delineate the quantitative contributions of different regions, both within South Korea and from overseas areas such as Beijing, Shandong, Shanghai, and Central China. Importantly, the results underscore the substantial influence of transboundary pollutant transport, emphasizing the need for international collaboration in addressing air quality issues. As metropolitan areas around the globe grapple with similar challenges, the methodology and insights from this study offer a potent tool and framework for regions seeking to understand and mitigate the impacts of O3 on human health and the environment. By integrating different sensitivity types, coupled with HYSPLIT backward trajectory analyses, this research equips policymakers with comprehensive data to design targeted interventions, emphasizing the significance of collaborative efforts in tackling regional air pollution challenges. However, it's important to note the limitation of this study, which is a case study conducted over a short time period. This constraint may impact the generalizability of the findings and suggests a need for further research to validate and expand upon these results.

A novel method of evaluating the non-invasive tear film break-up time and progression of corneal opacification in dogs using imaging video.

This study aimed to determine the correlation of the parameters that indicate the status of the ocular surface with the prognosis of corneal opacification. Fifty dogs (96 eyes) were examined using a grid-line illuminator (non-invasive tear film break-up time (NIBUT)). Thirty dogs (54 eyes) were included in the final analysis based on the criteria. The NIBUT and tear film break-up time (TFBUT) results of the eyes included in the study were divided into three groups: Group 1 (< 5 s), Group 2 (5 to <10 s), and Group 3 (≥ 10 s). The Schirmer's tear Test 1 (STT-1) results of the included patients were also divided into three groups: Group 1 (< 5 mm/min), Group 2 (5 to <10 mm/min), and Group 3 (≥ 10 mm/min). The corneal opacity grades are divided into four scores, ranging from 0 to 3. The corneal opacity grade score (COS) of 0 indicates a completely clear cornea or only a trace of opacity. COS of 1, 2, 3 indicate the presence of a prominent corneal opacity that does not interfere with the visualization of the fine iris details, the opacity obscures the visibility of the iris and lens details and severe obstruction of the intraocular structure visibility, respectively. The mean difference in COS during the follow-ups for each group of NIBUT were 0.61 ± 0.92 (n = 28), 0.10 ± 0.32 (n = 10), 0.19 ± 0.40 (n = 16). The NIBUT groups were significantly correlated with COS (p-value = 0.073) at a 10% level of significance. Post-hoc test at a 10% level of significance revealed significant correlations between Groups 1 and 2 (p-value = 0.041) and between Groups 1 and 3 (p-value 0.104). Although the TFBUT and STT-1 groups did not show any significant correlation with COS. Eyes with NIBUT of <5 s were found to have a significantly higher chance of increased COS compared with eyes with NIBUT of >5 s in the grid-line illumination plate NIBUT test. Among NIBUT, STT-1, and TFBUT, NIBUT was the only test that showed significant associations with the changes in COS.

Constraining East Asia ammonia emissions through satellite observations and iterative Finite Difference Mass Balance (iFDMB) and investigating its impact on inorganic fine particulate matter.

Uncertainty in ammonia (NH3) emissions causes the inaccuracy of fine particulate matter simulations, which is associated with human health. To address this uncertainty, in this work, we employ the iterative finite difference mass balance (iFDMB) technique to revise NH3 emissions over East Asia using the Cross-track Infrared Sounder (CRIS) satellite for July, August, and September 2019. Compared to the emissions, the revised NH3 emissions show an increase in China, particularly in the North China Plain (NCP) region, corresponding to agricultural land use in July, August, and September and a decrease in South Korea in September. The enhancement in NH3 emissions resulted in a remarkable increase in concentrations of NH3 by 5 ppb. in July and September, there is an increase in ammonium (NH4+) and nitrate (NO3-) concentrations by 5 µg/m3, particularly in the NCP region, while in August, both NH4+ and NO3- concentrations exhibit a decrease. For sulfate (SO42-), in August and September, the concentrations decreased over most regions of China and Taiwan, as a result of the production of ammonium sulfate; increased concentrations of SO42-, however, were simulated over South Korea, Japan, and the southern region of Chengdu, caused by higher relative humidity (RH). In contrast, during the month of July, our simulations showed an increase in SO42- concentrations over most regions of China. To gain a more comprehensive understanding, we defined a sulfur conversion ratio ( [Formula: see text] ), which explains how changes in sulfur in the gas phase affect changes in sulfate concentrations. A subsequent sensitivity analysis performed in this study indicated the same relationship between changes in ammonia and its effect on inorganic fine particulate matter (PM2.5). This study highlights the challenge of controlling and managing inorganic PM2.5 and indicates that reducing the emissions of air pollutants do not necessarily lead to a reduction in their concentrations.

Air quality and health co-benefits of vehicle electrification and emission controls in the most populated United States urban hubs: Insights from New York, Los Angeles, Chicago, and Houston.

Transitioning to electric vehicles (EVs) is a prominent strategy for reducing greenhouse gas emissions. However, given the complexity of atmospheric chemistry, the nuanced implications on air quality are yet to be fully understood. Our study delved into changes in PM2.5, ozone, and their associated precursors in major US urban areas, considering various electrification and mitigation scenarios. In the full electrification (FullE) scenario, PM2.5 reduction peaked at values between 0.34 and 2.29 µg.m-3 across distinct regions. Yet, certain areas in eastern Los Angeles exhibited a surprising uptick in PM2.5, reaching as much as 0.67 µg.m-3. This phenomenon was linked to a surge in secondary organic aerosols (SOAs), resulting from shifting NOx/VOCs (volatile organic compounds) dynamics and a spike in hydroxyl radical (OH) concentrations. The FullE scenario ushered in marked reductions in both NOx and maximum daily average 8-h (MDA8) ozone concentrations, with maximum levels ranging from 14.00 to 32.34 ppb and 2.58-9.58 ppb, respectively. However, certain instances revealed growths in MDA8 ozone concentrations, underscoring the intricacies of air quality management. From a health perspective, in the FullE scenario, New York, Chicago, and Houston stand to potentially avert 796, 328, and 157 premature deaths/month, respectively. Los Angeles could prevent 104 premature deaths/month in the HighE-BL scenario, representing a 29 % EV share for light-duty vehicles. However, the FullE scenario led to a rise in mortality in Los Angeles due to increased PM2.5 and MDA8 ozone levels. Economically, the FullE scenario projects health benefits amounting to 51-249 million $/day for New York, Chicago, and Houston. In contrast, Los Angeles may face economic downturns of up to 18 million $/day. In conclusion, while EV integration has the potential to improve urban air quality, offering substantial health and economic advantages, challenges persist. Our results emphasize the pivotal role of VOCs management, providing policymakers with insights for adaptable and efficient measures.

Assessing the effectiveness of artificial neural networks (ANN) and multiple linear regressions (MLR) in forcasting AQI and PM10 and evaluating health impacts through AirQ+ (case study: Tehran).

Air pollution is one of the major concerns for the population and the environment due to its hazardous effects. PM10 has affected significant scientific and regulatory interest because of its strong correlation with chronic health such as respiratory illnesses, lung cancer, and asthma. Forcasting air quality and assessing the health impacts of the air pollutants like particulate matter is crucial for protecting public health.This study incorporated weather, traffic, green space information, and time parameters, to forcst the AQI and PM10. Traffic data plays a critical role in predicting air pollution, as it significantly influences them. Therefore, including traffic data in the ANN model is necessary and valuable. Green spaces also affect air quality, and their inclusion in neural network models can improve predictive accuracy. The key factors influencing the AQI are the two-day lag time, the proximity of a park to the AQI monitoring station, the average distance between each park and AQI monitoring stations, and the air temperature. In addition, the average distance between each park, the number of parks, seasonal variations, and the total number of vehicles are the primary determinants affecting PM10.The straightforward effective Multilayer Perceptron Artificial Neural Network (MLP-ANN) demonstrated correlation coefficients (R) of 0.82 and 0.93 when forcasting AQI and PM10, respectively. This study also used the forcasted PM10 values from the ANN model to assess the health effects of elevated air pollution. The results indicate that elevated levels of PM10 can increase the likelihood of respiratory symptoms. Among children, there is a higher prevalence of bronchitis, while among adults, the incidence of chronic bronchitis is higher. It was estimated that the attributable proportions for children and adults were 6.87% and 9.72%, respectively. These results underscore the importance of monitoring air quality and taking action to reduce pollution to safeguard public health.

A comprehensive approach combining positive matrix factorization modeling, meteorology, and machine learning for source apportionment of surface ozone precursors: Underlying factors contributing to ozone formation in Houston, Texas.

Ozone concentrations in Houston, Texas, are among the highest in the United States, posing significant risks to human health. This study aimed to evaluate the impact of various emissions sources and meteorological factors on ozone formation in Houston from 2017 to 2021 using a comprehensive PMF-SHAP approach. First, we distinguished the unique sources of VOCs in each area and identified differences in the local chemistry that affect ozone production. At the urban station, the primary sources were n_decane, biogenic/industrial/fuel evaporation, oil and gas flaring/production, industrial emissions/evaporation, and ethylene/propylene/aromatics. At the industrial site, the main sources were industrial emissions/evaporation, fuel evaporation, vehicle-related sources, oil and gas flaring/production, biogenic, aromatic, and ethylene and propylene. And then, we performed SHAP analysis to determine the importance and impact of each emissions factor and meteorological variables. Shortwave radiation (SHAP values are ∼5.74 and ∼6.3 for Milby Park and Lynchburg, respectively) and humidity (∼4.87 and ∼4.71, respectively) were the most important variables for both sites. For the urban station, the most important emissions sources were n_decane (∼2.96), industrial emissions/evaporation (∼1.89), and ethylene/propylene/aromatics (∼1.57), while for the industrial site, they were oil and gas flaring/production (∼1.38), ethylene/propylene (∼1.26), and industrial emissions/evaporation (∼0.95). NOx had a negative impact on ozone production at the urban station due to the NOx-rich chemical regime, whereas NOx had positive impacts at the industrial site. The study's findings suggest that the PMF-SHAP approach is efficient, inexpensive, and can be applied to other similar applications to identify factors contributing to ozone-exceedance events. The study's results can be used to develop more effective air quality management strategies for Houston and other cities with high levels of ozone.

Sum frequency generation spectroscopy of fluorinated organic material-based interfaces: a tutorial review.

Molecular interactions at interfaces have a significant effect on the wetting properties of surfaces on a macroscale. Sum frequency generation (SFG) spectroscopy, one of a few techniques capable of probing such interactions, generates a surface vibrational spectrum sensitive to molecular structures and has been used to determine the orientation of molecules at interfaces. The purpose of this review is to assess SFG spectroscopy's ability to determine the molecular orientations of interfaces composed of fluorinated organic molecules. We will explore three different types of fluorinated organic material-based interfaces, naming liquid-air, solid-air, and solid-liquid interfaces, to see how SFG spectroscopy can be used to gain valuable and unique information regarding the molecular orientation of each interface. We hope this review will help to broaden the understanding of how to employ SFG spectroscopy to obtain more complex structural information for various fluorinated organic material-based interfaces in the future.

Large eddy simulation of sneeze plumes and particles in a poorly ventilated outdoor air condition: A case study of the University of Houston main campus.

Since the outbreak of the COVID-19 pandemic, many previous studies using computational fluid dynamics (CFD) have focused on the dynamics of air masses, which are believed to be the carriers of respiratory diseases, in enclosed indoor environments. Although outdoor air may seem to provide smaller exposure risks, it may not necessarily offer adequate ventilation that varies with different micro-climate settings. To comprehensively assess the fluid dynamics in outdoor environments and the efficiency of outdoor ventilation, we simulated the outdoor transmission of a sneeze plume in "hot spots" or areas in which the air is not quickly ventilated. We began by simulating the airflow over buildings at the University of Houston using an OpenFOAM computational fluid dynamics solver that utilized the 2019 seasonal atmospheric velocity profile from an on-site station. Next, we calculated the length of time an existing fluid is replaced by new fresh air in the domain by defining a new variable and selecting the hot spots. Finally, we conducted a large-eddy simulation of a sneeze in outdoor conditions and then simulated a sneeze plume and particles in a hot spot. The results show that fresh incoming air takes as long as 1000 s to ventilate the hot spot area in some specific regions on campus. We also found that even the slightest upward wind causes a sneeze plume to dissipate almost instantaneously at lower elevations. However, downward wind provides a stable condition for the plume, and forward wind can carry a plume even beyond six feet, the recommended social distance for preventing infection. Additionally, the simulation of sneeze droplets shows that the majority of the particles adhered to the ground or body immediately, and airborne particles can be transported more than six feet, even in a minimal amount of ambient air.

Quantifying the premature mortality and economic loss from wildfire-induced PM2.5 in the contiguous U.S.

Emissions from wildfires worsen air quality and can adversely impact human health. This study utilized the fire inventory from NCAR (FINN) as wildfire emissions, and performed air quality modeling of April-October 2012, 2013, and 2014 using the U.S. Environmental Protection Agency CMAQ model under two cases: with and without wildfire emissions. This study then assessed the health impacts and economic values attributable to PM2.5 from fires. Results indicated that wildfires could lead annually to 4000 cases of premature mortality in the U.S., corresponding to $36 billion losses. Regions with high concentrations of fire-induced PM2.5 were in the west (e.g., Idaho, Montana, and northern California) and Southeast (e.g., Alabama, Georgia). Metropolitan areas located near fire sources, exhibited large health burdens, such as Los Angeles (119 premature deaths, corresponding to $1.07 billion), Atlanta (76, $0.69 billion), and Houston (65, $0.58 billion). Regions in the downwind of western fires, although experiencing relatively low values of fire-induced PM2.5, showed notable health burdens due to their large population, such as metropolitan areas of New York (86, $0.78 billion), Chicago (60, $0.54 billion), and Pittsburgh (32, $0.29 billion). Results suggest that impacts from wildfires are substantial, and to mitigate these impacts, better forest management and more resilient infrastructure would be needed.

Deep learning mapping of surface MDA8 ozone: The impact of predictor variables on ozone levels over the contiguous United States.

The limited number of ozone monitoring stations imposes uncertainty in various applications, calling for accurate approaches to capturing ozone values in all regions, particularly those with no in-situ measurements. This study uses deep learning (DL) to accurately estimate daily maximum 8-hr average (MDA8) ozone and examines the spatial contribution of several factors on ozone levels over the contiguous U.S. (CONUS) in 2019. A comparison between in-situ observations and DL-estimated MDA8 ozone values shows a correlation coefficient (R) of 0.95, an index of agreement (IOA) of 0.97, and a mean absolute bias (MAB) of 2.79 ppb, highlighting the promising performance of the deep convolutional neural network (Deep-CNN) at estimating surface MDA8 ozone. Spatial cross-validation also confirms the high spatial accuracy of the model, which obtains an R of 0.91, and IOA of 0.96 and an MAB of 3.46 ppb when it is trained and tested on separate stations. To interpret the black-box nature of our DL model, we use Shapley additive explanations (SHAP) to generate a spatial feature contribution map (SFCM), the results of which confirm an advanced ability of Deep-CNN to capture the interactions between most predictor variables and ozone. For instance, the model shows that solar radiation (SRad) SFCM, with higher values, enhances the formation of ozone, particularly in the south and southwestern CONUS. As SRad triggers ozone precursors to produce ozone via photochemical reactions, it increases ozone concentrations. The model also shows that humidity, with its low values, increases ozone concentrations in the western mountainous regions. The negative correlation between humidity and ozone levels can be attributed to factors such as higher ozone decomposition resulting from increased levels of humidity and OH radicals. This study is the first to introduce the SFCM to investigate the spatial role of predictor variables on changes in estimated MDA8 ozone levels.

A Deep Convolutional Neural Network Model for Improving WRF Simulations.

Advancements in numerical weather prediction (NWP) models have accelerated, fostering a more comprehensive understanding of physical phenomena pertaining to the dynamics of weather and related computing resources. Despite these advancements, these models contain inherent biases due to parameterization of the physical processes and discretization of the differential equations that reduce simulation accuracy. In this work, we investigate the use of a computationally efficient deep learning (DL) method, the convolutional neural network (CNN), as a postprocessing technique that improves mesoscale Weather Research and Forecasting (WRF) one-day simulation (with a 1-h temporal resolution) outputs. Using the CNN architecture, we bias-correct several meteorological parameters calculated by the WRF model for all of 2018. We train the CNN model with a four-year history (2014-2017) to investigate the patterns in WRF biases and then reduce these biases in simulations for surface wind speed and direction, precipitation, relative humidity, surface pressure, dewpoint temperature, and surface temperature. The WRF data, with a spatial resolution of 27 km, cover South Korea. We obtain ground observations from the Korean Meteorological Administration station network for 93 weather station locations. The results indicate a noticeable improvement in WRF simulations in all station locations. The average of annual index of agreement for surface wind, precipitation, surface pressure, temperature, dewpoint temperature, and relative humidity of all stations is 0.85 (WRF:0.67), 0.62 (WRF:0.56), 0.91 (WRF:0.69), 0.99 (WRF:0.98), 0.98 (WRF:0.98), and 0.92 (WRF:0.87), respectively. While this study focuses on South Korea, the proposed approach can be applied for any measured weather parameters at any location.

Functionalized N-Heterocyclic Carbene Monolayers on Gold for Surface-Initiated Polymerizations.

Although N-heterocyclic carbenes (NHCs) are superior to thiol adsorbates in that they form remarkably stable bonds with gold, the generation of NHC-based self-assembled monolayers (SAMs) typically requires a strong base and an inert atmosphere, which limits the utility of such films in many applications. Herein, we report the development and use of bench-stable NHC adsorbates, benzimidazolium methanesulfonates, for the direct formation of NHC films on gold surfaces under an ambient atmosphere at room temperature without the need for extraordinary precautions. The generated NHC SAMs were fully characterized using ellipsometry, X-ray photoelectron spectroscopy (XPS), polarization modulation infrared reflection-absorption spectroscopy (PM-IRRAS), and contact angle measurements, and they were compared to analogous SAMs generated from an NHC bicarbonate adsorbate. Based on these findings, a unique radical initiator α,ω-bidentate azo-terminated NHC adsorbate, NHC15AZO[OMs], was designed and synthesized for the preparation of SAMs on gold surfaces with both NHC headgroups bound to the surface. The adsorbate molecules in NHC15AZO SAMs can exist in a hairpin or a linear conformation depending on the concentration of the adsorbate solution used to prepare the SAM. These conformations were studied by a combination of ellipsometry, XPS, PM-IRRAS, and scanning electron microscopy using gold nanoparticles (AuNPs) as a tag material. Moreover, the potential utility of these unique radical-initiating NHC films as surface-initiated polymerization platforms was demonstrated by controlling the thickness of polystyrene brush films grown from azo-terminated NHC monolayer surfaces simply by adjusting the reaction time of the photoinitiated radical polymer growth process.

Contributions of meteorology to ozone variations: Application of deep learning and the Kolmogorov-Zurbenko filter.

From hourly ozone observations obtained from three regions⸻Houston, Dallas, and West Texas⸻we investigated the contributions of meteorology to changes in surface daily maximum 8-h average (MDA8) ozone from 2000 to 2019. We applied a deep convolutional neural network and Shapely additive explanation (SHAP) to examine the complex underlying nonlinearity between variations of surface ozone and meteorological factors. Results of the models showed that between 2000 and 2019, specific humidity (38% and 27%) and temperature (28% and 37%) contributed the most to ozone formation over the Houston and Dallas metropolitan areas, respectively. On the other hand, the results show that solar radiation (50%) strongly impacted ozone variation over West Texas during this time. Using a combination of the Kolmogorov-Zurbenko (KZ) filter and multiple linear regression, we also evaluated the influence of meteorology on ozone and quantified the contributions of meteorological parameters to trends in surface ozone formation. Our findings showed that in Houston and Dallas, meteorology influenced ozone variations to a large extent. The impacts of meteorology on West Texas, however, showed meteorological factors had fewer influences on ozone variabilities from 2000 to 2019. This study showed that SHAP analysis and the KZ approach can investigate the contributions of the meteorological factors on ozone concentrations and help policymakers enact effective ozone mitigation policies.

The sensitivities of ozone and PM2.5 concentrations to the satellite-derived leaf area index over East Asia and its neighboring seas in the WRF-CMAQ modeling system.

Vegetation plays an important role as both a sink of air pollutants via dry deposition and a source of biogenic VOC (BVOC) emissions which often provide the precursors of air pollutants. To identify the vegetation-driven offset between the deposition and formation of air pollutants, this study examines the responses of ozone and PM2.5 concentrations to changes in the leaf area index (LAI) over East Asia and its neighboring seas, using up-to-date satellite-derived LAI and green vegetation fraction (GVF) products. Two LAI scenarios that examine (1) table-prescribed LAI and GVF from 1992 to 1993 AVHRR and 2001 MODIS products and (2) reprocessed 2019 MODIS LAI and 2019 VIIRS GVF products were used in WRF-CMAQ modeling to simulate ozone and PM2.5 concentrations for June 2019. The use of up-to-date LAI and GVF products resulted in monthly mean LAI differences ranging from -56.20% to 96.81% over the study domain. The increase in LAI resulted in the differences in hourly mean ozone and PM2.5 concentrations over inland areas ranging from 0.27 ppbV to -7.17 ppbV and 0.89 µg/m3 to -2.65 µg/m3, and the differences of those over the adjacent sea surface ranging from 0.69 ppbV to -2.86 ppbV and 3.41 µg/m3 to -7.47 µg/m3. The decreases in inland ozone and PM2.5 concentrations were mainly the results of dry deposition accelerated by increases in LAI, which outweighed the ozone and PM2.5 formations via BVOC-driven chemistry. Some inland regions showed further decreases in PM2.5 concentrations due to reduced reactions of PM2.5 precursors with hydroxyl radicals depleted by BVOCs. The reductions in sea surface ozone and PM2.5 concentrations were accompanied by the reductions in those in upwind inland regions, which led to less ozone and PM2.5 inflows. The results suggest the importance of the selective use of vegetation parameters for air quality modeling.

Changes in the ozone chemical regime over the contiguous United States inferred by the inversion of NOx and VOC emissions using satellite observation.

To investigate changes in the ozone (O3) chemical production regime over the contiguous United States (CONUS) with accurate knowledge of concentrations of its precursors, we applied an inverse modeling technique with Ozone Monitoring Instrument (OMI) tropospheric nitrogen dioxide (NO2) and total formaldehyde (HCHO) retrieval products in the summers of 2011, 2014, and 2017, years in which United States National Emission Inventory were based. The inclusion of dynamic chemical lateral boundary conditions and lightning-induced nitric oxide emissions significantly account for the contribution of background sources in the free troposphere. Satellite-constrained nitrogen oxide (NOx) and non-methane volatile organic compounds (NMVOCs) emissions mitigate the discrepancy between satellite and modeled columns: the inversion suggested 2.33-2.84 (1.07-1.34) times higher NOx over the CONUS (over urban regions) and 0.28-0.81 times fewer NMVOCs emissions over the southeastern United States. The model-derived HCHO/NO2 column ratio shows gradual spatial changes in the O3 production regime near urban cores relative to previously defined threshold values representing NOx and VOC sensitive conditions. We also found apparent shifts from the NOx-saturated regime to the transition regime (or the transition regime to the NOx-limited regime) over the major cities in the western United States. In contrast, rural areas, especially in the east-southeastern United States, exhibit a decreased HCHO/NO2 column ratio by -1.30 ± 1.71 with a reduction in HCHO column primarily driven by meteorology, becoming sensitive to VOC emissions. Results show that incorporating satellite observations into numerical modeling could help policymakers implement appropriate emission control policies for O3 pollution.

A comprehensive study of the COVID-19 impact on PM2.5 levels over the contiguous United States: A deep learning approach.

We investigate the impact of the COVID-19 outbreak on PM2.5 levels in eleven urban environments across the United States: Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, Phoenix, and Seattle. We estimate daily PM2.5 levels over the contiguous U.S. in March-May 2019 and 2020, and leveraging a deep convolutional neural network, we find a correlation coefficient, an index of agreement, a mean absolute bias, and a root mean square error of 0.90 (0.90), 0.95 (0.95), 1.34 (1.24) µg/m3, and 2.04 (1.87) µg/m3, respectively. Results from Google Community Mobility Reports and estimated PM2.5 concentrations show a greater reduction of PM2.5 in regions with larger decreases in human mobility and those in which individuals remain in their residential areas longer. The relationship between vehicular PM2.5 (i.e., the ratio of vehicular PM2.5 to other sources of PM2.5) emissions and PM2.5 reductions (R = 0.77) in various regions indicates that regions with higher emissions of vehicular PM2.5 generally experience greater decreases in PM2.5. While most of the urban environments ⸺ Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, and Seattle ⸺ show a decrease in PM2.5 levels by 21.1%, 20.7%, 18.5%, 8.05%, 3.29%, 3.63%, 6.71%, 4.82%, 13.5%, and 7.73%, respectively, between March-May of 2020 and 2019, Phoenix shows a 5.5% increase during the same period. Similar to their PM2.5 reductions, Washington DC, New York, and Boston, compared to other cities, exhibit the highest reductions in human mobility and the highest vehicular PM2.5 emissions, highlighting the great impact of human activity on PM2.5 changes in eleven regions. Moreover, compared to changes in meteorological factors, changes in pollutant concentrations, including those of black carbon, organic carbon, SO2, SO4, and especially NO2, appear to have had a significantly greater impact on PM2.5 changes during the study period.

A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance.

Issues regarding air quality and related health concerns have prompted this study, which develops an accurate and computationally fast, efficient hybrid modeling system that combines numerical modeling and machine learning for forecasting concentrations of surface ozone. Currently available numerical modeling systems for air quality predictions (e.g., CMAQ) can forecast 24 to 48 h in advance. In this study, we develop a modeling system based on a convolutional neural network (CNN) model that is not only fast but covers a temporal period of two weeks with a resolution as small as a single hour for 255 stations. The CNN model uses meteorology from the Weather Research and Forecasting model (processed by the Meteorology-Chemistry Interface Processor), forecasted air quality from the Community Multi-scale Air Quality Model (CMAQ), and previous 24-h concentrations of various measurable air quality parameters as inputs and predicts the following 14-day hourly surface ozone concentrations. The model achieves an average accuracy of 0.91 in terms of the index of agreement for the first day and 0.78 for the fourteenth day, while the average index of agreement for one day ahead prediction from the CMAQ is 0.77. Through this study, we intend to amalgamate the best features of numerical modeling (i.e., fine spatial resolution) and a deep neural network (i.e., computation speed and accuracy) to achieve more accurate spatio-temporal predictions of hourly ozone concentrations. Although the primary purpose of this study is the prediction of hourly ozone concentrations, the system can be extended to various other pollutants.