Surface NO Research Articles

Long-Term Impacts of COVID-19 Lockdown on the NO2 Concentrations and Urban Thermal Environment: Evidence from the Five Largest Urban Agglomerations in China

Under the threat of COVID-19, many regions around the world implemented lockdown policies to control the spread of the virus. This restriction on both social and economic activities has improved the quality of the environment in certain aspects. However, most previous studies have only focused on the short-term impact of lockdown policies on the urban environment. The long-term effects of lockdown require a more focused exploration and analysis. Thus, five major urban agglomerations in China were selected as the research area; changes in the numerical and spatial distribution of NO2 concentration and surface temperature during four different lockdown stages in 2019, 2020, and 2021 were investigated to analyze the long-term effects of lockdown policies on the urban environment. The results indicated that the impact of shorter lockdowns was short-term and unsustainable; the NO2 concentrations increased again with the resumption of production. Compared with air pollutants, thermal environmental problems are more complex. The effect of the lockdown policy was not reflected in the decrease in the area proportions of the high- and sub-high-temperature regions but rather in the spatial distribution of the high-temperature area, which was manifested as a fragmentation and dispersion of heat source patches. In addition to the severity of the lockdown, the impact of the lockdown policy was also closely related to the level of development and industrial structure of each city. Among the urban environments of the five agglomerations, the most affected were the Yangtze River Delta and Yangtze River Middle-Reach urban agglomerations, which had the largest decline in NO2 concentrations and the most notable fragmentation of heat source patches.

Combined Effect of High-Resolution Land Cover and Grid Resolution on Surface NO2 Concentrations

High-resolution air quality simulations are often performed using different nested domains and resolutions. In this study, the variability of nitrogen dioxide (NO2) concentrations estimated from two nested domains focused on Portugal (D2 and D3), with 5 and 1 km horizontal grid resolutions, respectively, was investigated by applying the WRF-Chem model for the year 2015. The main goal and innovative aspect of this study is the simulation of a whole year with high resolutions to analyse the spatial variability under the simulation grids in conjunction with detailed land cover (LC) data specifically processed for these high-resolution domains. The model evaluation was focused on Portuguese air quality monitoring stations taking into consideration the station typology. As main results, it should be noted that (i) D3 urban LC categories enhanced pollution hotspots; (ii) generally, modelled NO2 was underestimated, except for rural stations; (iii) differences between D2 and D3 estimates were small; (iv) higher resolution did not impact model performance; and (v) hourly D2 estimates presented an acceptable quality level for policy support. These modelled values are based on a detailed LC classification (100 m horizontal resolution) and coarse spatial resolution (approximately 10 km) emission inventory, the latter suitable for portraying background air pollution problems. Thus, if the goal is to characterise urban/local-scale pollution patterns, the use of high grid resolution could be advantageous, as long as the input data are properly represented.

Estimation and Spatiotemporal Analysis of NO2 Pollution in East Asia During 2001–2016

AbstractAmbient nitrogen dioxide (NO2) not only has adverse health effects on humans but also contributes to the production of two major secondary atmospheric pollutants, ozone (O3) and fine particulate matter (PM2.5). In this study, surface NO2 concentrations in East Asia from 2001 to 2016 were estimated by combining an ensemble backpropagation neural network method, satellite NO2 column data, and reanalysis data. The estimated monthly and annual mean NO2 concentrations were well‐correlated with the observations, with R (correlation coefficient) values of 0.89 and 0.91, respectively. Our results indicate that the NO2 concentrations in most areas of East Asia peaked during 2011–2013. The NO2 concentrations in autumn and winter, especially in the eastern and northern parts of China, were much higher than those in summer. In terms of population NO2 exposure, over 25 million South Korea residents (∼45% of the population) were exposed to NO2 concentrations higher than the 2005 World Health Organization's annual standard (40 μg/m3, ∼22 ppbv at 25°C) in 2015. In contrast, the entire populations of some developing countries, such as Vietnam, Cambodia, Myanmar, the Philippines, and Lao PDR, were exposed to NO2 concentrations less than 14 ppbv. Based on the estimation, NO2‐related asthma cases in East Asia increased by 1.37% annually from 2001 to 2015, reaching 139,000 cases (95% confidence interval: 37,400–263,400) in 2015. NOx emission inventories vary from country to country in East Asia; thus, more targeted NOx emission‐control policies are urgently required.

First satellite-based regional hourly NO2 estimations using a space-time ensemble learning model: A case study for Beijing-Tianjin-Hebei Region, China

Surface Nitrogen dioxide (NO2) concentrations have been generated with satellite retrievals using multiple statistical algorithms. However, they are often given at coarse frequencies (“snapshot”, daily or even longer), limiting their applications in epidemiological studies and assessing the evolution of NO2 pollution. This study investigated the potential applicability of Himawari-8 derived hourly fine particulate matter concentrations in producing hourly NO2 concentrations by constructing a space-time ensemble model. The Beijing-Tianjin-Hebei (BTH) region, one of the serious pollution regions in China, is the study region chosen. The proposed model performs well in estimating hourly NO2 concentration with a high cross-validation (CV) coefficient of determination (R2 = 0.81) and low CV root-mean-square (RMSE = 9.71 μg/m3), mean prediction errors (MPE = 6.33 μg/m3), and relative prediction errors (RPE = 22.5%). On daily, monthly, seasonal, and annual time scales, CV R2 increases to 0.89, 0.93, 0.97, and 0.99, respectively. The annual mean model estimated NO2 concentration over BTH region is 28.2 ± 6.5 μg/m3, with relatively higher NO2 concentrations are seen in southern and southeastern BTH. Winter experiences the most severe NO2 concentrations, followed by autumn, spring, and summer. Surface NO2 concentrations are higher (lower) in the morning (afternoon) and tend to decrease gradually with time. The model generally captures the hourly evolution of NO2 concentrations for the severe pollution episode but shows some underestimations. The annual mean NO2 concentrations were 2.8% lower on the weekend than on weekdays. In addition, the weekend effects of NO2 concentrations are larger at rush hour and lower in the noon. The hourly NO2 products derived from proposed approach are potentially useful for improving our understanding of the source, evolution, and transportation behavior of NO2 pollution episodes and for exposure- and health-related research. The proposed approach also enriches the potential applications of geostationary satellites (e.g., Himawari-8).

Long-term trends in urban NO2 concentrations and associated paediatric asthma incidence: estimates from global datasets

Combustion-related nitrogen dioxide (NO2) air pollution is associated with paediatric asthma incidence. We aimed to estimate global surface NO2 concentrations consistent with the Global Burden of Disease study for 1990-2019 at a 1 km resolution, and the concentrations and attributable paediatric asthma incidence trends in 13 189 cities from 2000 to 2019. We scaled an existing annual average NO2 concentration dataset for 2010-12 from a land use regression model (based on 5220 NO2 monitors in 58 countries and land use variables) to other years using NO2 column densities from satellite and reanalysis datasets. We applied these concentrations in an epidemiologically derived concentration-response function with population and baseline asthma rates to estimate NO2-attributable paediatric asthma incidence. We estimated that 1·85 million (95% uncertainty interval [UI] 0·93-2·80 million) new paediatric asthma cases were attributable to NO2 globally in 2019, two thirds of which occurred in urban areas (1·22 million cases; 95% UI 0·60-1·8 million). The proportion of paediatric asthma incidence that is attributable to NO2 in urban areas declined from 19·8% (1·22 million attributable cases of 6·14 million total cases) in 2000 to 16·0% (1·24 million attributable cases of 7·73 million total cases) in 2019. Urban attributable fractions dropped in high-income countries (-41%), Latin America and the Caribbean (-16%), central Europe, eastern Europe, and central Asia (-13%), and southeast Asia, east Asia, and Oceania (-6%), and rose in south Asia (+23%), sub-Saharan Africa (+11%), and north Africa and the Middle East (+5%). The contribution of NO2 concentrations, paediatric population size, and asthma incidence rates to the change in NO2-attributable paediatric asthma incidence differed regionally. Despite improvements in some regions, combustion-related NO2 pollution continues to be an important contributor to paediatric asthma incidence globally, particularly in cities. Mitigating air pollution should be a crucial element of public health strategies for children. Health Effects Institute, NASA.

Hybrid Deep Learning Models for Mapping Surface No2 Across China: One Complicated Model, Many Simple Models, or Many Complicated Models?

Hybrid Deep Learning Models for Mapping Surface No2 Across China: One Complicated Model, Many Simple Models, or Many Complicated Models?

Hybrid Deep Learning Models for Mapping Surface No2 Across China: One Complicated Model, Many Simple Models, or Many Complicated Models?

Hybrid Deep Learning Models for Mapping Surface No2 Across China: One Complicated Model, Many Simple Models, or Many Complicated Models?

Adsorption Kinetics of NO2 Gas on Pt/Cr-TiO2/Pt-Based Sensors

Metal oxides are excellent candidates for the detection of various gases; however, the issues such as the limited operating temperature and selectivity are the most important ones requiring the comprehensive understanding of gas adsorption kinetics on the sensing layer surfaces. To this context, the present study focuses mainly on the fabrication of a Pt/Cr-TiO2/Pt type sensor structure that is highly suitable in reducing the operating temperature (from 400 to 200 °C), extending the lower limit NO2 gas concentration (below 10 ppm) with fast response (37 s) and recovery (24 s) times. This illustrates that the sensor performance is not only solely dependent on the nature of sensing material, but also, it is significantly enhanced by using such a new kind of electrode geometry. Moreover, Cr doping into TiO2 culminates in altering the sensor response from n- to p-type and thus contributes to sensor performance enhancement by detecting low NO2 concentrations selectively at reduced operating temperatures. In addition, the NO2 surface adsorption kinetics are studied by fitting the obtained sensor response curves with Elovich, inter-particle diffusion, and pseudo first-order and pseudo second-order adsorption models. It is found that a pseudo first-order reaction model describes the best NO2 adsorption kinetics toward 7–170 ppm NO2 gas at 200 °C. Finally, the sensing mechanism is discussed on the basis of the obtained results.

Unraveling pathways of elevated ozone induced by the 2020 lockdown in Europe by an observationally constrained regional model using TROPOMI.

Questions about how emissions are changing during the COVID-19 lockdown periods cannot be answered by observations of atmospheric trace gas concentrations alone, in part due to simultaneous changes in atmospheric transport, emissions, dynamics, photochemistry, and chemical feedback. A chemical transport model simulation benefiting from a multi-species inversion framework using well-characterized observations should differentiate those influences enabling to closely examine changes in emissions. Accordingly, we jointly constrain NO x and VOC emissions using well-characterized TROPOspheric Monitoring Instrument (TROPOMI) HCHO and NO2 columns during the months of March, April, and May 2020 (lockdown) and 2019 (baseline). We observe a noticeable decline in the magnitude of NO x emissions in March 2020 (14 %-31 %) in several major cities including Paris, London, Madrid, and Milan, expanding further to Rome, Brussels, Frankfurt, Warsaw, Belgrade, Kyiv, and Moscow (34 %-51 %) in April. However, NO x emissions remain at somewhat similar values or even higher in some portions of the UK, Poland, and Moscow in March 2020 compared to the baseline, possibly due to the timeline of restrictions. Comparisons against surface monitoring stations indicate that the constrained model underrepresents the reduction in surface NO2. This underrepresentation correlates with the TROPOMI frequency impacted by cloudiness. During the month of April, when ample TROPOMI samples are present, the surface NO2 reductions occurring in polluted areas are described fairly well by the model (model: -21 ± 17 %, observation: -29 ± 21 %). The observational constraint on VOC emissions is found to be generally weak except for lower latitudes. Results support an increase in surface ozone during the lockdown. In April, the constrained model features a reasonable agreement with maximum daily 8 h average (MDA8) ozone changes observed at the surface (r = 0.43), specifically over central Europe where ozone enhancements prevail (model: +3.73 ± 3.94 %, + 1.79 ppbv, observation: +7.35 ± 11.27 %, +3.76 ppbv). The model suggests that physical processes (dry deposition, advection, and diffusion) decrease MDA8 surface ozone in the same month on average by -4.83 ppbv, while ozone production rates dampened by largely negative become less negative, leading ozone to increase by +5.89 ppbv. Experiments involving fixed anthropogenic emissions suggest that meteorology contributes to 42 % enhancement in MDA8 surface ozone over the same region with the remaining part (58 %) coming from changes in anthropogenic emissions. Results illustrate the capability of satellite data of major ozone precursors to help atmospheric models capture ozone changes induced by abrupt emission anomalies.

Estimating NOx LOTOS-EUROS CTM Emission Parameters over the Northwest of South America through 4DEnVar TROPOMI NO2 Assimilation

In this work, we present the development of a 4D-Ensemble-Variational (4DEnVar) data assimilation technique to estimate NOx top-down emissions using the regional chemical transport model LOTOS-EUROS with the NO2 observations from the TROPOspheric Monitoring Instrument (TROPOMI). The assimilation was performed for a domain in the northwest of South America centered over Colombia, and includes regions in Panama, Venezuela and Ecuador. In the 4DEnVar approach, the implementation of the linearized and adjoint model are avoided by generating an ensemble of model simulations and by using this ensemble to approximate the nonlinear model and observation operator. Emission correction parameters’ locations were defined for positions where the model simulations showed significant discrepancies with the satellite observations. Using the 4DEnVar data assimilation method, optimal emission parameters for the LOTOS-EUROS model were estimated, allowing for corrections in areas where ground observations are unavailable and the region’s emission inventories do not correctly reflect the current emissions activities. The analyzed 4DEnVar concentrations were compared with the ground measurements of one local air quality monitoring network and the data retrieved by the satellite instrument Ozone Monitoring Instrument (OMI). The assimilation had a low impact on NO2 surface concentrations reducing the Mean Fractional Bias from 0.45 to 0.32, primordially enhancing the spatial and temporal variations in the simulated NO2 fields.

Time series analysis and spatial distribution map of aggregate risk index due to tropospheric NO2 and O3 based on satellite observation

A high increase in human activities has led to more emission of air pollutants in metropolises and industrial areas. Recently, remotely sensed data of tropospheric pollutants is used for environmental management and decision-making on large scale. The purpose of this study was a time series analysis of nitrogen dioxide Vertical Column Density (NO2 VCD) and Ozone (O3) using Ozone Monitoring Instrument (OMI) from 2005 to 2016 by Mann-Kendall test. Also, the aggregate risk index (ARI) was calculated to estimate the overall impact of exposure to tropospheric NO2 and O3 concentrations at the national scale in 2016. To estimate the surface NO2 related drivers, The Radial Basis Function (RBF) neural network modeling was performed for different months of 2016. Results of Mann-Kendall test showed that tropospheric ozone concentration had an increasing trend in all parts of Iran and this increasing trend was significantly higher in the southern region of Iran and lower in the northern parts of Iran. NO2 VCD in most parts of Iran had a significant increasing trend. The result of sensitivity analysis showed that NO2 VCD (1.25), the distance to the industrial area, (1.20) and wind speed (1.07) were the most important variables for the estimation of surface NO2 concentration. Spatial ARI with the highest risks is mainly located in the Northern half of Iran, especially in Tehran, Alborz, and Khorasan-e− Razavi provinces, where NO2 and O3 concentrations are very severe. In northern Iran and central cities, the ARI values are calculated from 1.5 to 2.08, indicating the highest human health risks in these regions. The human health risks based on OMI observation were obtained higher in comparison to AQM data because the satellite data coverage is larger than AQM station and monitors transmitted air pollution by the wind in addition to local pollution. Based on this research, using satellite observation for air quality monitoring is a suitable tool for environmental management on a national scale.

Long-term trend of ozone in southern China reveals future mitigation strategy for air pollution

The 14-year trends of surface ozone were examined using observations from 12 urban sites and 4 regional sites over the Pearl River Delta (PRD) region in China over the 2006–2019 period. Surface ozone exhibits upward trends in the range of 0.28–1.02 ppb yr−1 at urban sites and mixed trends in the range of −0.29 – 0.21 ppb yr−1 at regional sites. Meanwhile, surface Ox (Ox = O3+NO2) mixing ratios exhibit downward trends at 9 urban sites and 3 regional sites in the range of −0.01 to −0.54 ppb yr−1, revealing that the ozone rise at urban sites is predominantly attributed to the reduced titration effect by nitric oxide (NO). The further decline of surface NO2 mixing ratios could drive the decreases in Ox mixing ratios and the increases in ozone mixing ratios at urban sites. In addition, surface Ox concentrations show a good correlation with aerosol concentrations, implying the dominant contributions of secondary production to aerosol and the consistent control strategies of ozone and aerosol pollution in PRD. For the abatement of ozone pollution, our results highlight the synergistic control of key precursors including nitrogen oxides and volatile organic compounds to counteract the reduced NO titration effect by inhibiting the photochemical production of ozone. At last, we also highlight the importance of establishing more regional sites in comprehensively assessing spatiotemporal changes in surface air pollutants in China.

Improving predictability of high-ozone episodes through dynamic boundary conditions, emission refresh and chemical data assimilation during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign

Abstract. Although air quality in the United States has improved remarkably in the past decades, ground-level ozone (O3) often rises in exceedance of the national ambient air quality standard in nonattainment areas, including the Long Island Sound (LIS) and its surrounding areas. Accurate prediction of high-ozone episodes is needed to assist government agencies and the public in mitigating harmful effects of air pollution. In this study, we have developed a suite of potential forecast improvements, including dynamic boundary conditions, rapid emission refresh and chemical data assimilation, in a 3 km resolution Community Multiscale Air Quality (CMAQ) modeling system. The purpose is to evaluate and assess the effectiveness of these forecasting techniques, individually or in combination, in improving forecast guidance for two major air pollutants: surface O3 and nitrogen dioxide (NO2). Experiments were conducted for a high-O3 episode (28–29 August 2018) during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign, which provides abundant observations for evaluating model performance. The results show that these forecast system updates are useful in enhancing the capability of this 3 km forecasting model with varying effectiveness for different pollutants. For O3 prediction, the most significant improvement comes from the dynamic boundary conditions derived from the NOAA operational forecast system, National Air Quality Forecast Capability (NAQFC), which increases the correlation coefficient (R) from 0.81 to 0.93 and reduces the root mean square error (RMSE) from 14.97 to 8.22 ppbv, compared to that with the static boundary conditions (BCs). The NO2 from all high-resolution simulations outperforms that from the operational 12 km NAQFC simulation, regardless of the BCs used, highlighting the importance of spatially resolved emission and meteorology inputs for the prediction of short-lived pollutants. The effectiveness of improved initial concentrations through optimal interpolation (OI) is shown to be high in urban areas with high emission density. The influence of OI adjustment, however, is maintained for a longer period in rural areas, where emissions and chemical transformation make a smaller contribution to the O3 budget than that in high-emission areas. Following the assessment of individual updates, the forecasting system is configured with dynamic boundary conditions, optimal interpolation of initial concentrations and emission adjustment, to simulate a high-ozone episode during the 2018 LISTOS field campaign. The newly developed forecasting system significantly reduces the bias of surface NO2 prediction. When compared with the NASA Langley GeoCAPE Airborne Simulator (GCAS) vertical column density (VCD), this system is able to reproduce the NO2 VCD with a higher correlation (0.74), lower normalized mean bias (40 %) and normalized mean error (61 %) than NAQFC (0.57, 45 % and 76 %, respectively). The 3 km system captures magnitude and timing of surface O3 peaks and valleys better. In comparison with lidar, O3 profile variability of the vertical O3 is captured better by the new system (correlation coefficient of 0.71) than by NAQFC (correlation coefficient of 0.54). Although the experiments are limited to one pollution episode over the Long Island Sound, this study demonstrates feasible approaches to improve the predictability of high-O3 episodes in contemporary urban environments.

Contribution of Vehicle Emission and NO2 Surface Conversion to Nitrous Acid (HONO) in Urban Environments: Implications from Tests in a Tunnel.

Nitrous acid (HONO) is an important photochemical precursor to hydroxyl radicals particularly in an urban atmosphere, yet its primary emission and secondary production are often poorly constrained. Here, we measured HONO and nitrogen oxides (NOx) at both the inlet and the outlet in a busy urban tunnel (>30 000 vehicles per day) in south China. Multiple linear regression revealed that 73.9% of the inlet-outlet incremental HONO concentration was explained by NO2 surface conversion, while the rest was directly emitted from vehicles with an average HONO/NOx ratio of 1.31 ± 0.87%, which was higher than that from previous tunnel studies. The uptake coefficient of NO2, γ(NO2), on the tunnel surfaces was calculated to be (7.01 ± 0.02) × 10-5, much higher than that widely used in models. As tunnel surfaces are typical of urban surfaces in the wall and road materials, the dominance of HONO from surface reactions in the poorly lit urban tunnel demonstrated the importance of NO2 conversion on urban surfaces, instead of NO2 conversion on the aerosol surface, for both daytime and night-time HONO even in polluted ambient air. The higher γ(NO2) on urban surfaces and the elevated HONO/NOx ratio from this study can help explain the missing HONO sources in urban areas.

Deep Learning Estimation of Daily Ground‐Level NO2 Concentrations From Remote Sensing Data

AbstractThe limited number of nitrogen dioxide (NO2) surface measurements calls for the development of highly accurate approaches to estimating surface NO2 concentrations. In this study, we leverage a new satellite instrument, the TROPOspheric Monitoring Instrument (TROPOMI), along with other predictor variables, to estimate daily surface NO2 concentrations over Texas in 2019. We use the deep convolutional neural network (Deep‐CNN), an advanced deep learning algorithm, to obtain estimates and achieve a correlation coefficient (R) of 0.91, an index of agreement (IOA) of 0.95, and a mean absolute bias (MAB) of 1.75 ppb in surface NO2 estimation. Additionally, we leverage a novel approach, shapley additive explanations (SHAP), to describe how Deep‐CNN understands each predictor variable. The SHAP results show that the Deep‐CNN model has an advanced understanding of the data set, revealing that TROPOMI closely captures levels of NO2. In addition, we show the superiority of our Deep‐CNN model at estimating surface NO2 over other well‐known machine learning and regression models in the field, including the support vector machines (SVM), random forest (RF), and multiple linear regression (MLR). Although SVM and RF show strong capabilities at estimating surface NO2 concentrations, their accuracy is inferior to that of the Deep‐CNN model, ranking second and third in model accuracy in this study. The MLR, however, shows a poor ability at NO2 estimation and ranks last among all models. Testing the impact of sample size on model performance, we also show that, compared to other models, Deep‐CNN needs more samples to trigger its strength at surface NO2 estimation.

Application of Satellite Observations and Air Quality Modelling to Validation of NOx Anthropogenic EMEP Emissions Inventory over Central Europe

One of the most important minor species in the atmosphere is nitrogen dioxide (NO2). The primary objective of the presented research was to propose a method to adjust emission inventories (emission fluxes) using tropospheric NO2 columns observed by OMI and SCIAMACHY instruments. Modified emission fluxes were used in a chemical weather model GEM-AQ. The GEM-AQ model results were compared with the monthly averaged satellite-derived column amount of NO2 over Europe for the 2008–2010 observing period. It was shown that the observed and modelled spatial distribution of high values of the NO2 column is highly correlated with the distribution of major anthropogenic sources in the modelling domain. The presented findings highlight the importance of the anthropogenic sources in the overall budget of NO2 in the polluted troposphere. Regions for which modelling results showed underestimation or overestimation compared with observations were constant for the whole analysis period. Thus, the NO2 column observations could be used for correcting emission estimates. The proposed emission correction method is based on the differences in modelled and satellite-derived NO2 columns. Modelling was done for 2011 using the original and adjusted emission inventories and compared with observed NO2 columns. The analysis was extended to compare modelling results with surface NO2 observations from selected air quality stations in Poland. A significant improvement in modelling results was obtained over regions with large overestimations in the control run for which the original emission fluxes were used.

State-to-State Transition Study of the Exchange Reaction for N(4S) and O2(X3Σg-) Collision by Quasi-Classical Trajectory.

Based on the new 2A' and 4A' potential energy surfaces of NO2 fitted by Varga et al., we conducted a quasi-classical trajectory study on the N(4S) +O2(X3Σg- ) → NO(2Π) + O(3P) reaction, focusing on the high vibrational state up to ν = 25. For different rovibrational states of O2, within the relative translational energy (Ec) range of 0.1-30 eV, the total exchange cross section (ECS) is calculated, and it is found that the initial relative translational energy and vibration excitation have a significant effect on ECSs, while rotational excitation has little influence; the rate coefficient of the high rovibrational state of O2 molecules at high temperatures is studied, and it is found that when the vibrational level ν of O2 is in the range of 0-15, the value of log10 k(T, ν, j) with the vibrational level ν is almost linear, while when ν is greater than 15, it becomes gentle with the increase in ν. Finally, the state-to-state rate coefficients are calculated; our results supply the advantageous state-to-state process data in the NO2 system, and they are useful for further studying the related hypersonic gas flow at very high temperature.

Effect of Lockdown on Pollutant Levels in the Delhi Megacity: Role of Local Emission Sources and Chemical Lifetimes

The COVID-19 pandemic resulted in changed emission regimes all over the world. India also imposed complete lockdown on all modes of travel and industrial activities for about 2 months from 25-March-2020 and later unlocked these activities in a phased manner. Here, we study signatures of emissions changes on levels of atmospheric trace gases and aerosols contributing to air pollution over multiple sites in India’s capital Delhi covering various lockdown and unlock phases using satellite data and in-situ observations. The resulting changes in the levels of these species were compared with respect to their average of 2015–2019 to attribute for year to year and seasonal changes. A clear impact of lockdown was observed for AOD, PM, NO2, CO, and SO2 as a result of emission changes, while changed precursor levels led to a change in O3 chemical regimes impacting its concentrations. A detailed analysis of FLEXPART trajectories revealed increased PM levels over Delhi in north-westerly air masses sourced to Punjab region all the way up to Pakistan. Changes in aerosols and NO2 were not only restricted to the surface but transcended the total tropospheric column. The maximum decrease in PM, NO2, CO, and SO2 was observed during the month of total lockdown in April. The lockdown impact varied with species e.g., PM10 and PM2.5 as well as locations even within the periphery of Delhi. While surface level aerosols and NO2 showed significant and almost similar changes, AOD showed much lower decrease than tropospheric column NO2.

Isotopic evidence for dominant secondary production of HONO in near-ground wildfire plumes

Abstract. Nitrous acid (HONO) is an important precursor to hydroxyl radical (OH) that determines atmospheric oxidative capacity and thus impacts climate and air quality. Wildfire is not only a major direct source of HONO, it also results in highly polluted conditions that favor the heterogeneous formation of HONO from nitrogen oxides (NOx= NO + NO2) and nitrate on both ground and particle surfaces. However, these processes remain poorly constrained. To quantitatively constrain the HONO budget under various fire and/or smoke conditions, we combine a unique dataset of field concentrations and isotopic ratios (15N / 14N and 18O / 16O) of NOx and HONO with an isotopic box model. Here we report the first isotopic evidence of secondary HONO production in near-ground wildfire plumes (over a sample integration time of hours) and the subsequent quantification of the relative importance of each pathway to total HONO production. Most importantly, our results reveal that nitrate photolysis plays a minor role (<5 %) in HONO formation in daytime aged smoke, while NO2-to-HONO heterogeneous conversion contributes 85 %–95 % to total HONO production, followed by OH + NO (5 %–15 %). At nighttime, heterogeneous reduction of NO2 catalyzed by redox active species (e.g., iron oxide and/or quinone) is essential (≥ 75 %) for HONO production in addition to surface NO2 hydrolysis. Additionally, the 18O / 16O of HONO is used for the first time to constrain the NO-to-NO2 oxidation branching ratio between ozone and peroxy radicals. Our approach provides a new and critical way to mechanistically constrain atmospheric chemistry and/or air quality models on a diurnal timescale.

Impact of urban imperviousness on boundary layer meteorology and air chemistry on a regional scale

It has been long understood that land cover change from natural to impervious modifies the surface energy balance and hence the dynamical properties of the overlying atmosphere. The urban heat island is manifested in the formation of an urban boundary layer with distinct thermodynamic features that in turn govern transport processes of air pollutants. While many studies already demonstrated the benefits of urban canopy models (UCM) for atmospheric modelling, work on the impact on urban air chemistry is scarce. This study uses the state-of-the-art coupled chemistry-climate modelling system MECO(n) to assess the impact of the COSMO UCM TERRA_URB on the dynamics and gas phase chemistry in the boundary layer of the urban agglomeration Rhine-Main in Germany. Comparing the model results to ground observations and satellite and ground based remote sensing data, we found that the UCM experiment reduces the bias in temperature at the surface and throughout the boundary layer. This is true for ground level NO2 and ozone distribution as well. The application of MECO(n) for urban planning purposes is discussed by designing case studies representing two projected scenarios in future urban planning – densification of central urban areas and urban sprawl. Averaged over the core urban region and 10‑days during a heat wave period in July 2018, model results indicate a warming of 0.7 K in surface temperature and 0.2 K in air temperature per 10 % increase in impervious surface area fraction. Within this period, a 50 % total increase of imperviousness accounts for a 3 K and 1 K spatially averaged warming respectively. This change in thermodynamic features results in a decrease of surface NO2 concentration by 10–20 % through increased turbulent mixing in areas with highest impervious fraction and highest emissions. In the evening and nighttime however, increased densification in the urban centre results in amplified canyon blocking, which in turn results in average increase in near surface NO2 concentrations of about 10 %, compared to the status quo. This work intends to analyse regional scale features of surface-atmosphere interactions in an urban boundary layer and can be seen as preparatory work for higher resolution street scale models.