Variation Of NO Research Articles

Variation of Tropospheric NO2 on the Territories of Saint Petersburg and Leningrad Region According to Remote Sensing Data

Variation of Tropospheric NO2 on the Territories of Saint Petersburg and Leningrad Region According to Remote Sensing Data

Spatial Pattern of Air Pollutant Concentrations and Their Relationship with Meteorological Parameters in Coastal Slum Settlements of Lagos, Southwestern Nigeria

This study assessed the spatial disposition of air pollutants and their relationship with meteorological parameters in urban slum settlements of Lagos city. The gaseous pollutants were quantified using a gas analyzer, and the PM2.5 concentration and meteorological parameters were determined using an Air Metric Sampler and Wind Mate, respectively. SPSS for Windows and ArcGIS were used for data analysis. The results revealed that the seasonal variations in SO2, NO2, CO2, and PM2.5 showed a higher level of air pollutant concentration during the dry season than during the wet season. During the wet season, a significant correlation was observed between PM2.5 and temperature at the 1% level (0.957 **), and VOC and SO2 (0.907 *) at the 5% level; during the dry season, significant correlations were observed between NO2 and SO2 at the 1% level (0.9477 **), and PM2.5 and relative humidity (0.832 *) at the 5% level. Atmospheric pressure (72%), temperature (60%), and relative humidity (98.4) were the primary meteorological factors affecting air pollutants such as VOC, CO2, and SO2. The spatial dispersal of air pollutants revealed a high Z score and a moderate p-value, indicating hot spot locations throughout the five selected slum settlements. It is recommended that regular monitoring based on quantifiable economic costs that are beneficial to the well-being of the populace be investigated, and policy-based initiatives for air pollution control based on scientific evidence be advocated for.

Estimating 2013–2019 NO2 exposure with high spatiotemporal resolution in China using an ensemble model

Air pollution has become a major issue in China, especially for traffic-related pollutants such as nitrogen dioxide (NO2). Current studies in China at the national scale were less focused on NO2 exposure and consequent health effects than fine particulate exposure, mainly due to a lack of high-quality exposure models for accurate NO2 predictions over a long period. We developed an advanced modeling framework that incorporated multisource, high-quality predictor data (e.g., satellite observations [Ozone Monitoring Instrument NO2, TROPOspheric Monitoring Instrument NO2, and Multi-Angle Implementation of Atmospheric Correction aerosol optical depth], chemical transport model simulations, high-resolution geographical variables) and three independent machine learning algorithms into an ensemble model. The model contains three stages: (1) filling missing satellite data; (2) building an ensemble model and predicting daily NO2 concentrations from 2013 to 2019 across China at 1×1 km2 resolution; (3) downscaling the predictions to finer resolution (100 m) at the urban scale. Our model achieves a high performance in terms of cross-validation to assess the agreement of the overall (R2 = 0.72) and the spatial (R2 = 0.85) variations of the NO2 predictions over the observations. The model performance remains moderately good when the predictions are extrapolated to the previous years without any monitoring data (CV R2 > 0.68) or regions far away from monitors (CV R2 > 0.63). We identified a clear decreasing trend of NO2 exposure from 2013 to 2019 across the country with the largest reduction in suburban and rural areas. Our downscaled model further improved the prediction ability by 4%–14% in some megacities and captured substantial NO2 variations within 1-km grids in the urban areas, especially near major roads. Our model provides flexibility at both temporal and spatial scales and can be applied to exposure assessment and epidemiological studies with various study domains (e.g., national or citywide) and settings (e.g., long-term and short-term).

Characterizing and classifying urban watersheds with compositional and structural attributes

AbstractCurrent land‐use classifications used to assess urbanization effects on stream water quality date back to the 1980s when limited information was available to characterize watershed attributes that mediate non‐point source pollution. With high resolution remote sensing and widely used GIS tools, there has been a vast increase in the availability and precision of geospatial data of built environments. In this study, we leverage geospatial data to expand the characterization of developed landscapes and create a typology that allows us to better understand the impact of complex developed landscapes across the rural to urban gradient. We assess the ability of the developed landscape typology to reveal patterns in stream water chemistry previously undetected by traditional land‐cover based classification. We examine the distribution of land‐cover, infrastructure, topography and geology across 3876 National Hydrography Dataset Plus catchments in the Piedmont region of North Carolina, USA. From this dataset, we generate metrics to evaluate the abundance, density and position of landscape features relative to streams, catchment outlets and topographic wetness metrics. While impervious surfaces are a key distinguishing feature of the urban landscape, sanitary infrastructure, population density and geology are better predictors of baseflow stream water chemistry. Unsupervised clustering was used to generate a distinct developed landscape typology based on the expanded, high‐resolution landscape feature information. Using stream chemistry data from 37 developed headwater catchments, we compared the baseflow water chemistry grouped by traditional land‐cover based classes of urbanization (rural, low, medium and high density) to our composition and structure‐based classification (a nine‐class typology). The typology based on 22 metrics of developed landscape composition and structure explained over 50% of the variation in NO3−‐N, TDN, DOC, Cl−, and Br− concentration, while the ISC‐based classification only significantly explained 23% of the variation in TDN. These results demonstrate the importance of infrastructure, population and geology in defining developed landscapes and improving discrete classes for water management.

Nitrogen dioxide decline and rebound observed by GOME-2 and TROPOMI during COVID-19 pandemic

Since its first confirmed case in December 2019, coronavirus disease 2019 (COVID-19) has become a worldwide pandemic with more than 90 million confirmed cases by January 2021. Countries around the world have enforced lockdown measures to prevent the spread of the virus, introducing a temporal change of air pollutants such as nitrogen dioxide (NO2) that are strongly related to transportation, industry, and energy. In this study, NO2 variations over regions with strong responses to COVID-19 are analysed using datasets from the Global Ozone Monitoring Experiment-2 (GOME-2) sensor aboard the EUMETSAT Metop satellites and TROPOspheric Monitoring Instrument (TROPOMI) aboard the EU/ESA Sentinel-5 Precursor satellite. The global GOME-2 and TROPOMI NO2 datasets are generated at the German Aerospace Center (DLR) using harmonized retrieval algorithms; potential influences of the long-term trend and seasonal cycle, as well as the short-term meteorological variation, are taken into account statistically. We present the application of the GOME-2 data to analyze the lockdown-related NO2 variations for morning conditions. Consistent NO2 variations are observed for the GOME-2 measurements and the early afternoon TROPOMI data: regions with strong social responses to COVID-19 in Asia, Europe, North America, and South America show strong NO2 reductions of sim 30–50% on average due to restriction of social and economic activities, followed by a gradual rebound with lifted restriction measures.

Spatial variation of outdoor and indoor NO2 in metropolitan cities in India

BACKGROUND AND AIM: Exposure to traffic related pollutants is known to affect the cardio-respiratory health in urban population. The residential urban neighborhoods in low- and middle-income countries such as India are in close proximity to major roads, densely packed, and likely have high heterogeneity in air pollution exposures. However, there is very limited data on long-term exposures to air pollutants and their intra-urban spatial variability in such countries. Thus, this study aims to examine the spatial variation of outdoor NO2, a proxy of traffic air pollution, in metropolitan cities and its relationship with indoor NO2 levels. METHODS: As part of a multi-centric adult women cohort, biweekly averaged outdoor NO2 was measured in the winter at ~60 residential locations each in three metropolitan cities in India – Delhi, Mumbai, and Bengaluru - using passive samplers. Indoor NO2 was also measured at half of these residences. Household characteristics including indoor activities, likely sources and ventilation conditions were also collected through structured questionnaire surveys. RESULTS:Mean (±SD) outdoor NO2 were 58.5±8.8, 57.9±15.8 29.5±8.1 μg/m3 for Delhi, Mumbai and Bengaluru, respectively. Corresponding indoor concentrations were 61.8±18.2, 69.6±20.1 and 53.2±21.1 μg/m3 for the three cities, respectively. Indoor/Outdoor (I/O) ratios of 1.03±0.3 (Delhi), 1.25±0.3 (Mumbai) and 1.82±0.6 (Bangalore) suggest the strong influence of the indoor sources coupled with poor ventilation, in particular in Mumbai and Bengaluru. Moderate to poor correlation was observed between indoor and outdoor NO2 (Pearson R=0.36, 0.49 and 0.52 respectively). Socio-economic factors, building airtightness, cooking activities as well as occupant’s behaviour (e.g. opening windows) are also identified as key factors influencing the I/O ratios. CONCLUSIONS:Our findings show substantial differences in outdoor NO2 between and within the cities, the influence of indoor pollution sources and activities on indoor levels. Further investigation delineating the influence of indoor and outdoor sources is underway. KEYWORDS: Nitrogen dioxide, Exposure assessment, Long-term exposures, Socio-economic factors.

Estimating 2005-2019 NO2 Exposure with High Spatiotemporal Resolution in China Using an Ensemble Model

BACKGROUND AND AIM: China faces serious air pollution problems, especially for traffic-related pollutants such as nitrogen dioxide (NO₂). Epidemiological studies in China were much less focused on NO₂ exposure and consequent health effects as compared to fine particles exposure at national scale, mainly due to lack of high-quality exposure model for accurate NO₂ predictions over a long period. We aim to develop a national model in estimating long-term NO₂ exposure in China with high spatiotemporal resolution. METHODS: We proposed an advanced modeling framework that incorporated multisource, high-quality predictors data (e.g., satellite observations [OMI NO₂, TROPOMI NO₂, and MAIAC AOD], chemical transport model simulations, high-resolution geographical variables) and three independent machine learning algorithms into an ensemble model. The model included three-stages: (1) filling missing satellite data; (2) building an ensemble model and predicting daily NO₂ levels from 2005 to 2019 across China at 1-km resolution; (3) downscaling the predictions to 100-meter resolution at city scale. RESULTS:Our model achieved a high performance with an overall cross-validated (CV) R² of 0.72 and a spatial R² of 0.85. The model has the potential to be extrapolated to previous years (2005-2012) or regions without monitoring data, with moderately good performance (CV R² 0.68). We identified a clear decreasing trend of NO₂ exposure from 2005 to 2019 across the country with the largest reduction in suburban and rural areas. Our downscaled model further improved the prediction ability by 4% in some megacities and captured substantial NO₂ variations within 1-km grids in urban areas especially near major roads. CONCLUSIONS:We developed a NO₂ national model with very high spatiotemporal resolution (daily, 1-km grid). Our model provided flexibility at both temporal and spatial scales that can be useful for exposure assessment and epidemiological studies with various study domains (e.g. national or citywide) and settings (e.g. long-term and short-term). KEYWORDS: air pollution, oxides of nitrogen, exposure assessment, long-term exposure, modeling

Continental and Ecoregion‐Specific Drivers of Atmospheric NO2 and NH3 Seasonality Over Africa Revealed by Satellite Observations

AbstractAmmonia (NH3) and nitrogen oxides (NOx: nitrogen dioxide [NO2] + nitric oxide [NO]) play important roles in atmospheric chemistry. Throughout most of Africa, emissions of these gases are predominantly from soils and biomass burning. Here we use observations of tropospheric NO2 vertical column densities (VCDs) from the Ozone Monitoring Instrument from 2005 through 2017 and atmospheric NH3 VCDs from the Infrared Atmospheric Sounding Interferometer from 2008 through 2017 to evaluate seasonal variation of NO2 and NH3 VCDs across Africa and in seven African ecoregions. In regions where mean annual precipitation (MAP) is under 500 mm yr−1, we find that NO2 and NH3 VCDs are positively related to monthly precipitation, and where MAP is between 500 and 1,750 mm yr−1 or higher, NO2 VCDs are negatively related to monthly precipitation. In dry ecoregions, temperature and precipitation were important predictors of NH3 and NO2 VCDs, likely related to variation in soil emissions. In mesic ecoregions, monthly NO2 VCDs were strongly related to burned area, suggesting that biomass burning drives seasonality. NH3 VCDs in mesic ecoregions were positively related to both monthly temperature and monthly carbon monoxide (CO) VCDs, suggesting that a mixture of soil and biomass burning emissions influenced NH3 seasonality. In northern mesic ecoregions, monthly temperature explained most of the variance in monthly NH3 VCDs, suggesting that soil sources, including animal excreta, determined NH3 seasonality. In southern mesic ecoregions, monthly CO VCDs explained more variation in NH3 VCDs than temperature, suggesting that biomass burning may have greater influence over NH3 seasonality.

Comprehensive evaluations of diurnal NO2 measurements during DISCOVER-AQ 2011: effects of resolution-dependent representation of NO x emissions.

Nitrogen oxides (NO x =NO+NO2) play a crucial role in the formation of ozone and secondary inorganic and organic aerosols, thus affecting human health, global radiation budget, and climate. The diurnal and spatial variations in NO2 are functions of emissions, advection, deposition, vertical mixing, and chemistry. Their observations, therefore, provide useful constraints in our understanding of these factors. We employ a Regional chEmical and trAnsport model (REAM) to analyze the observed temporal (diurnal cycles) and spatial distributions of NO2 concentrations and tropospheric vertical column densities (TVCDs) using aircraft in situ measurements and surface EPA Air Quality System (AQS) observations as well as the measurements of TVCDs by satellite instruments (OMI: the Ozone Monitoring Instrument; GOME-2A: Global Ozone Monitoring Experiment - 2A), ground-based Pandora, and the Airborne Compact Atmospheric Mapper (ACAM) instrument in July 2011 during the DISCOVER-AQ campaign over the Baltimore-Washington region. The model simulations at 36 and 4 km resolutions are in reasonably good agreement with the regional mean temporospatial NO2 observations in the daytime. However, we find significant overestimations (underestimations) of model-simulated NO2 (O3) surface concentrations during night-time, which can be mitigated by enhancing nocturnal vertical mixing in the model. Another discrepancy is that Pandora-measured NO2 TVCDs show much less variation in the late afternoon than simulated in the model. The higher-resolution 4 km simulations tend to show larger biases compared to the observations due largely to the larger spatial variations in NO x emissions in the model when the model spatial resolution is increased from 36 to 4 km. OMI, GOME-2A, and the high-resolution aircraft ACAM observations show a more dispersed distribution of NO2 vertical column densities (VCDs) and lower VCDs in urban regions than corresponding 36 and 4 km model simulations, likely reflecting the spatial distribution bias of NO x emissions in the National Emissions Inventory (NEI) 2011.

Using land-use machine learning models to estimate daily NO2 concentration variations in Taiwan

It is likely that exposure surrogates from monitoring stations with various limitations are not sufficient for epidemiological studies covering large areas. Moreover, the spatiotemporal resolution of air pollution modelling approaches must be improved in order to achieve more accurate estimates. If not, the exposure assessments will not be applicable in future health risk assessments. To deal with this challenge, this study featured Land-Use Regression (LUR) models that use machine learning to assess the spatial-temporal variability of Nitrogen Dioxide (NO2). Daily average NO2 data was collected from 70 fixed air quality monitoring stations, belonging to the Taiwanese EPA, on the main island of Taiwan. Around 0.41 million observations from 2000 to 2016 were used for the analysis. Several datasets were employed to determine spatial predictor variables, including the EPA environmental resources dataset, the meteorological dataset, the land-use inventory, the landmark dataset, the digital road network map, the digital terrain model, MODIS Normalized Difference Vegetation Index database, and the power plant distribution dataset. Regarding analyses, conventional LUR and Hybrid Kriging-LUR were performed first to identify important predictor variables. A Deep Neural Network, Random Forest, and XGBoost algorithms were then used to fit the prediction model based on the variables selected by the LUR models. Lastly, data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were applied to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 65% and 78%, respectively, of NO2 variation. When the XGBoost algorithm was further incorporated in LUR and hybrid-LUR, the explanatory power increased to 84% and 91%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed all other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm to estimate the spatial-temporal variability of NO2 exposure. For practical application, the associations of specific land-use/land cover types selected in the final model can be applied in land-use management and in planning emission reduction strategies.

Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning

Nitrogen dioxide (NO2) is an important air pollutant with negative health effects and a precursor of ozone and particulate matter responsible for photo-chemical smog and wintertime air pollution. To evaluate human exposure to NO2 for public health assessment, maps of near-surface NO2 concentrations at a high resolution of 100 m are desirable. In this study, we report hourly maps of gridded near-surface NO2 concentrations that are produced using an extreme gradient-boosted tree ensemble for an Alpine domain (Switzerland and northern Italy) spanning two years, from June 2018 to May 2020. To estimate the NO2 distribution at ground level, we used satellite observations of NO2 vertical column density, land use data, meteorological fields and topographical information to train models with in situ NO2 ground measurements. The best model with this approach captured up to 59% of hourly NO2 variation for 40 test stations in the domain with a mean absolute error of 7.69μg/m3, performing especially well for urban regions with dense sampling. We present the first hourly maps of NO2 concentrations that reveal previously unresolved spatio-temporal variations. Local interpretations of the machine learning model demonstrate that TROPOMI NO2 satellite observations make a strong contribution to the information content of the near-surface NO2 maps besides their relatively coarse resolution (3.5 × 5.5 km2) and the fact that they are only available once a day under cloud-free conditions. The COVID-19 pandemic lockdown presents a case study that offers new insights into the importance of satellite data that can partially re-mediate statistical models' unsusceptibility to unusual events (like changes due to political intervention) with regard to model training.

ANALYSIS OF NO2 TROPOSPHERIC COLUMN AMOUNT AT AIRPORTS IN MALAYSIA BEFORE AND DURING COVID-19 PANDEMIC USING SENTINEL-5P TROPOMI DATA

Abstract. Air pollution is a serious environmental and health issue in Malaysia due to the recent urbanization processes. The main sources of air pollutants are motorized vehicles in urban areas and airports and industrial activities. At the airports, NO2 is the main pollutant of concern besides aerosols particles, yet gap in data availability prevent studies to describe their patterns and quantify their effects on human health and climate change. In this study NO2 data from TROPOMI sensor on board Sentinel 5-P satellite was used to characterize the spatial and temporal patterns of NO2 tropospheric column amounts at major airports in Malaysia. The results demonstrate that NO2 amounts from aircrafts and ground traffic activities are generally higher and/or similar to the amounts found in urban areas. Total tropospheric column amounts of NO2 during the movement restriction imposed due to Covid-19 pandemic between March and April 2020 was approximately 50% lower the total emission during the same period in 2019 (representing a business as usual period). Assessing the spatial pattern and temporal variations in NO2 (both surface and total vertical profile) is important for monitoring the impact of air pollutants on climate change and human health in Malaysia.

Removing the effects of meteorological factors on changes in nitrogen dioxide and ozone concentrations in China from 2013 to 2020

Previous studies on long-term ozone (O3) variations in China have reported inconsistent conclusions on the role of meteorological factors in controlling said variations. In this study, we used an observation-based decomposition model to conduct an up-to-date investigation of the effects of meteorological factors on the variations in nitrogen dioxide (NO2) and O3 concentrations in China in the summer from 2013 to 2020. The variations in NO2 and O3 concentrations after removing the major meteorological effects were then analyzed to improve our understanding of O3 formation regimes. Ground measurements show that both NO2 and O3 concentrations decreased in eastern, central, and southeastern China (e.g., NO2 and O3 concentrations in Wuhan reduced by 4.3 and 6.2 ppb, respectively), which was not anticipated. Analyses of meteorological effects showed that reduced wind strength, decreased temperature, and increased relative humidity significantly reduced O3 concentrations in eastern and central China (e.g., by 10.5 ppb in Wuhan). After removing the major meteorological effects, the O3 trends were reversed in eastern and central China (e.g., increased by 4.9 ppb in Wuhan). The contrasting trends in NO2 and O3 concentrations suggest that their O3 formations were sensitive to volatile organic compounds (VOC-limited regime). In southeastern China, both NO2 and O3 concentrations decreased, implying that the O3 formation regimes changed to mixed sensitive or nitrogen oxide-limited (NOx-limited) regimes. The meteorological effects varied by region and may play a dominant role in controlling the long-term O3 variation. Our results indicate that the attribution of O3 variation to emission control without accounting for meteorological effects can be misleading.

Dissolved oxygen disturbs nitrate transformation by modifying microbial community, co-occurrence networks, and functional genes during aerobic-anoxic transition

No consensus has been achieved among researchers on the effect of dissolved oxygen (DO) on nitrate (NO3−-N) transformation and the microbial community, especially during aerobic-anoxic transition. To supplement this knowledge, NO3−-N transformation, microbial communities, co-occurrence networks, and functional genes were investigated during aerobic-anoxic transition via microcosm simulation. NO3−-N transformation rate in the early stage (DO ≥2 mg/L) was always significantly higher than that in the later stage (DO <2 mg/L) during aerobic-anoxic transition, and NO2−-N accumulation was more significant during the anoxic stage, consistent with the result obtained under constant DO conditions. These NO3−-N transformation characteristics were not affected by other environmental factors, indicating the important role of DO in NO3−-N transformation during aerobic-anoxic transition. Changes in DO provoked significant alterations in microbial diversity and abundance of functional bacteria dominated by Massilia, Bacillus, and Pseudomonas, leading to the variation in NO3−-N transformation. Co-occurrence network analysis revealed that NO3−-N transformation was performed by the interactions between functional bacteria including symbiotic and competitive relationship. In the presence of oxygen, these interactions accelerated the NO3−-N transformation rate, and bacterial metabolization proceeded via increasingly varied pathways including aerobic and anoxic respiration, which was demonstrated through predicted genes. The higher relative abundance of genes narG, narH, and napA suggested the occurrence of coupled aerobic-anoxic denitrification in the early stage. NO3−-N transformation rate decreased accompanied by a significant NO2−-N accumulation with the weakening of coupled aerobic-anoxic denitrification during aerobic-anoxic transition. Structural equation modeling further demonstrated the relationship between DO and NO3−-N transformation. DO affects NO3−-N transformation by modifying microbial community, bacterial co-occurrence, and functional genes during aerobic-anoxic transition.

Effects of urban functional fragmentation on nitrogen dioxide (NO2) variation with anthropogenic-emission restriction in China

Urban functional fragmentation plays an important role in assessing Nitrogen Dioxide (NO2) emissions and variations. While the mediated impact of anthropogenic-emission restriction has not been comprehensively discussed, the lockdown response to the novel coronavirus disease 2019 (COVID-19) provides an unprecedented opportunity to meet this goal. This study proposes a new idea to explore the effects of urban functional fragmentation on NO2 variation with anthropogenic-emission restriction in China. First, NO2 variations are quantified by an Autoregressive Integrated Moving Average with external variables-Dynamic Time Warping (SARIMAX-DTW)-based model. Then, urban functional fragmentation indices including industrial/public Edge Density (ED) and Landscape Shape Index (LSI), urban functional Aggregation Index (AI) and Number of Patches (NP) are developed. Finally, the mediated impacts of anthropogenic-emission restriction are assessed by evaluating the fragmentation-NO2 variation association before and during the lockdown during COVID-19. The findings reveal negative effects of industrial ED, public LSI, urban functional AI and NP and positive effects of public ED and industrial LSI on NO2 variation based on the restricted anthropogenic emissions. By comparing the association analysis before and during lockdown, the mediated impact of anthropogenic-emission restriction is revealed to partially increase the effect of industrial ED, industrial LSI, public LSI, urban functional AI and NP and decrease the effect of public ED on NO2 variation. This study provides scientific findings for redesigning the urban environment in related to the urban functional configuration to mitigating the air pollution, ultimately developing sustainable societies.

Influences of Work Function Changes in NO2 and H2S Adsorption on Pd-Doped ZnGa2O4(111) Thin Films: First-Principles Studies

The work function variations of NO2 and H2S molecules on Pd-adsorbed ZnGa2O4(111) were calculated using first-principle calculations. For the bonding of a nitrogen atom from a single NO2 molecule to a Pd atom, the maximum work function change was +1.37 eV, and for the bonding of two NO2 molecules to a Pd atom, the maximum work function change was +2.37 eV. For H2S adsorption, the maximum work function change was reduced from −0.90 eV to −1.82 eV for bonding sulfur atoms from a single and two H2S molecules to a Pd atom, respectively. Thus, for both NO2 and H2S, the work function change increased with an increase in gas concentration, showing that Pd-decorated ZnGa2O4(111) is a suitable material in NO2/H2S gas detectors.

Feature analysis on air quality in the main urban area of Nanchong City in 2015–2018

This study evaluates the air quality in Nanchong City in 2015–2018 based on automatic monitoring data and meteorological data in the main urban area. Results show that overall air quality in the main urban area of Nanchong was standard and improved from 2015 to 2018. SO2, NO2, and CO had slight influences on air quality in the study area, whereas excessive annual average PM2.5 and increasing O3 concentration challenged the air quality. The monthly average mass concentrations of particulate matters (PM2.5 and PM10), NO2, and CO generally presented a “U-shaped” distribution pattern while O3 showed the opposite. The diurnal variation of PM, NO2, and CO has two peaks related to the morning and evening rush hours. Vehicle exhaust is the main source of local pollution. Over-standard PM2.5 can significantly inhibit O3 production while O3 can easily promote the secondary generation of PM2.5 due to its strong oxidization. PM2.5 emission should be controlled strictly in winter, while in summer, the sources of O3 should be controlled vigorously. Air pollution is comprehensive and complicated and is influenced by various factors, mainly meteorological and human factors. Measures such as artificial rainfall should be included at the appropriate time to cope with adverse meteorological conditions when heavy pollution occurs in winter.

The regulation effect of methane and hydrogen on the emission characteristics of ammonia/air combustion in a model combustor

Ammonia, made up of 17.8% hydrogen, has attracted a lot of attention in combustion community due to its zero carbon emission as a fuel in gas turbines. However, ammonia combustion still faces some challenges including the weak combustion and sharp NOx emissions which discourage its application. It was demonstrated that the combustion intensity of ammonia/air flame can be enhanced through adding active fuels like methane and hydrogen, while the NOx emission issue will emerge in the meantime. This study investigates regulation effect of methane and hydrogen on the emission characteristics of ammonia/air flame in a gas turbine combustor. The instantaneous OH profile and global emissions at the combustion chamber outlet are measured with Planar Laser Induced Fluorescence (PLIF) technique and the Fourier Transform Infrared (FTIR), respectively. The flames are also simulated by large eddy simulation to further reveal physical and chemical processes of the emissions formation. Results show that for NH3/air flames, the emissions behavior of the gas turbine combustor is similar to the calculated one-dimensional flames. Moreover, the NOx emissions and the unburned NH3 can be simultaneously controlled to a proper value at the equivalence ratio (φ) of approximate 1.1. The variation of NO and NO2 with φ for NH3/H2/air flames and NH3/CH4/air flames at blending ratio (Zf) of 0.1 are similar to the NH3/air flames, with the peak moving towards rich condition. This indicates that the NH3/air flame can be regulated through adding a small amount of active fuels without increasing the NOx emission level. However, when Zf = 0.3, we observe a clear large NOx emission and CO for NH3/CH4/air flames, indicating H2 is a better choice on the emission control. The LES results show that NO and OH radicals exhibit a general positive correlation. And the temperature plays a secondary role in promoting NOx formation comparing with CH4/air flame.

Variation of tropospheric NO2 over Indo-Gangetic plain during COVID-19 outbreak in India

Nitrogen dioxide (NO2) is a gaseous air pollutant which primarily gets in the air when fossil fuels such as coal, oil, gas or diesel are burned at high temperatures through industries, power plant, automobiles and locomotive. Continuous exposure to elevated concentration of NO2 may impose development of asthma and potentially increase in susceptibility to respiratory infections in humans. The present study examined impact of lockdown due to COVID-19 in terms of spatial and temporal variation of tropospheric NO2 in Indo-Gangetic plain. The results showed steep reduction in tropospheric NO2 as a result of ceased industrial and economic activities. Analysis of Sentinel-5P satellite data was carried out for specific period during 2019–2020 at a regional scale and for major industrial cities. Tremendous positive impact of lockdown in atmospheric condition is observed, wherein NO2 concentration has gone down by 20–40% in all major cities except few exceptions were noticed. The concomitant impact of Covid-19 has ensued pragmatic constructive impact on environment and climate, due to drop in NO2 emissions.

Estimating daily full-coverage near surface O3, CO, and NO2 concentrations at a high spatial resolution over China based on S5P-TROPOMI and GEOS-FP

The Near Surface Concentrations (NSC) of O3, CO, and NO2 are crucial worldwide indicators of air quality. However, current frameworks devised for the estimation of the NSC of O3, CO, and NO2 have defects, such as coarse spatial resolution and large missing coverage. To address this issue, this study aims to estimate the daily (~13:30 local time) full-coverage NSC of O3, CO, and NO2 at a high spatial resolution (0.05° for O3 and NO2; 0.07° for CO) over China by using datasets from S5P-TROPOMI and GEOS-FP. In specific, the light gradient boosting machine is employed to train the estimation models. Validation results show that the NSC of O3, CO, and NO2 are well estimated, with the R2s of 0.91, 0.71, and 0.83 for the sample-based cross validation, respectively. Meanwhile, the proposed framework achieves a satisfactory performance in comparison to the latest related works, as reflected by the estimation accuracy and spatial resolution. As for the mapping, the estimated results show coherent spatial distribution and can accurately grasp the seasonal characteristics of each air pollutant. Finally, the estimated results are utilized to analyze the temporal variations of O3, CO, and NO2 during the COrona VIrus Disease 2019 (COVID-19) lockdown in China, which is an extend application for adopting the proposed framework in air quality monitoring. Results show that the estimated NSC of O3, CO, and NO2 in 2020 present significant variations during different periods of the COVID-19 lockdown in China compared to last year. In addition, the variations in the NSC of O3, CO, and NO2 during the COVID-19 lockdown in China possibly result from restrictions in the anthropogenic activities.