NO2 Concentrations Research Articles

NOx Prediction of Supersonic Coherent Jets for Electric Arc Steelmaking Furnace

Supersonic coherent jets are widely in practice in steelmaking processes including electric arc furnaces (EAF). Injecting oxygen through such coherent jets plays a vital role in enhancing the liquid gas mixing and reaction rates leading to boosting energy efficiency. Several experiments and numerical simulations have been carried out to understand the physical phenomenon of the coherent jets and to predict the behavior of the jets. However, the research on pollutant formation in the coherent jets for steelmaking is limited. As the industry transitions toward reducing emissions, prediction of pollutant formation is crucial. This numerical study analyzes both methane shrouding coherent jets and hydrogen shrouding coherent jets and predicts the NOx (oxides of nitrogen) formation. It has been found that NO concentration dominates over N2O and NO2 and has the highest concentration around the jet. Although the NO concentration reaches as high as 1070 ppm at high-temperature region around the jet, it remains below 225 ppm along the fuel inlet axis. Also, it has found that the NOx concentrations increase along the radial direction up to 1.5De\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.5{\ ext{De}}$$\\end{document} from the jet central line and gradually decreases, whereas the NO concentration peaks at 15De\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$15{\ ext{D}}_{{\ ext{e}}}$$\\end{document} along the jet axis in the downstream direction.

N-3 Polyunsaturated Fatty Acids Are Associated with Stable Nitric Oxide Metabolites in Highly Trained Athletes.

The aim of this study was to investigate the relationships between levels of n-3 essential polyunsaturated fatty acids (n-3 PUFAs) and stable nitric oxide (NO) metabolites in the plasma of athletes. Highly trained cross-country skiers (males, n = 39) were examined. The fatty acid profile of the total plasma lipids was determined by gas chromatography. The plasma NO level was studied by a colorimetric method via reaction with Griess reagent. A widespread deficiency of essential n-3 PUFAs in the plasma of athletes (more than 80% of the subjects) was demonstrated in association with an imbalance in the levels of nitrates (NO3) and nitrites (NO2). A lower value of n-3 linolenic acid in the plasma (0.21 mol/%) was associated with a NO3 level below the normal range (n-3 C18:3 and NO3 Rs = 0.461; p = 0.003). Higher levels of n-3 eicosapentaenoic acid (0.8 mol/%) were associated with a concentration of NO2 above the normal value (n-3 C20:5 and NO2 Rs = 0.449; p = 0.004). For the first time, the participation of essential n-3 PUFAs in the nitrite-nitrate pathway of NO synthesis in highly trained skiers was demonstrated.

Federated Bayesian network approach for cross-regional air pollution classification: a case study of the Beijing-Tianjin-Hebei region.

Although machine learning methods have enabled considerable progress in air quality assessment, challenges persist regarding data privacy, cross-regional data processing, and model generalization. To address these issues, we introduce an advanced federated Bayesian network (FBN) approach. By integrating federated learning, adaptive optimization algorithms, and homomorphic encryption technologies, we substantially enhanced the efficiency and security of cross-regional air quality data processing. The novelty of this research lies in the improvements implemented in federated learning for air quality data analysis, particularly in distributed model training optimization and data consistency. Through the integration of adaptive structural modification strategies and simulated annealing immune optimization algorithms, we markedly enhanced the structural learning accuracy of the Bayesian network, resulting in a 20% improvement in prediction accuracy. Moreover, employing homomorphic encryption ensured data transmission security and confidentiality. In our Beijing-Tianjin-Hebei case study, our method demonstrated a 15% improvement in air quality classification accuracy compared to conventional methods and exhibited superior interpretability in analyzing environmental factor interactions. We quantified complex air pollution patterns across regions and found that a 30% fluctuation in the air quality index correlated with NO2 concentrations. We also observed a moderate positive correlation between specific pollutant indicators in Hebei Province and Tianjin and changes in air quality. Additionally, the FBN exhibited better operational efficiency and data confidentiality than other machine learning models in handling large-scale and multisource environmental data. Our FBN approach presents a novel perspective for environmental monitoring and assessment, vital for understanding complex air pollution patterns and formulating future ecological protection policies.

Study on Novel SCR Catalysts for Denitration of High Concentrated Nitrogen Oxides and Their Reaction Mechanisms

With the rapid development of industrialization, the emission of nitrogen oxides (NOx) has become a global environmental issue. Uranium is the primary fuel used in nuclear power generation. However, the production of uranium, typically based on the uranyl nitrate method, usually generates large amounts of nitrogen oxides, particularly NO2, with concentrations in the exhaust gas exceeding 10,000 ppm. High concentrations of nitrogen dioxide are also produced during silver electrolysis processing and the treatment of waste electrolyte solutions. Traditional V-W/TiO2 NH3-SCR catalysts typically exhibit high catalytic activity at temperatures ranging from 300 to 400 °C, under conditions of low NOx concentrations and high gas hourly space velocity. However, their performance is not satisfying when reducing high concentrations of NO2. This study aims to optimize the traditional V-W/TiO2 catalysts to enhance their catalytic activity under conditions of high NO2 concentrations (10,000 ppm) and a wide temperature range (200–400 °C). On the basis of 3 wt% Mo/TiO2, various loadings of V2O5 were selected, and their catalytic activities were tested. Subsequently, the optimal ratios of active component vanadium and additive molybdenum were explored. Simultaneously, doping with WO3 for modification was selected in the V-Mo/TiO2 catalyst, followed by activity testing under the same conditions. The results show that: the NOx conversion rates of all five catalysts increase with temperature at range of 200–400 °C. Excessive loading of MoO3 decreased the catalytic performance, with 5 wt% being the optimal loading. The addition of WO3 significantly enhanced the low-temperature activity of the catalysts. When the loadings of WO3 and MoO3 were both 3 wt%, the catalyst exhibited the best denitrification performance, achieving a NOx conversion rate of 98.8% at 250 °C. This catalyst demonstrates excellent catalytic activity in reducing very high concentration (10,000 ppm) NO2, at a wider temperature range, expanding the temperature range by 50% compared to conventional SCR catalysts. Characterization techniques including BET, XRD, XPS, H2-TPR, and NH3-TPD were employed to further study the evolution of the catalyst, and the promotional mechanisms are explored. The results revealed that the proportion of chemisorbed oxygen (Oα) increased in the WO3-modified catalyst, exhibiting lower V reduction temperatures, which are favorable for low-temperature denitrification activity. NH3-TPD experiments showed that compared to MoOx species, surface WOx species could provide more acidic sites, resulting in stronger surface acidity of the catalyst.

Levels and health risk assessments of particulate matter and inorganic gaseous pollutants in urban and industrial areas of Hawassa city, Ethiopia

Air pollution is a global public health concern due to its harmful health effects. However, there is scarce data on concentrations and sources of inorganic gaseous pollutants (NO2, CO, and SO2) and particulate matter (PM2.5 and PM10) in Ethiopia, particularly Hawassa City. Thus, the goal of this research is to determine the indoor and outdoor concentrations of PM2.5, PM10, NO2, CO, and SO2 in urban and industrial areas of Hawassa City, Ethiopia, and to evaluate potential health concerns. A portable gas monitor device (HoldPeak Laser PM meter, HP 5800D) was used to measure the levels of PM10 and PM2.5. The Aeroqual Series 500 Portable Air Quality Monitor (Aeroqual Ltd., New Zealand) was used to measure the concentrations of NO2, CO, and SO2. The results of this study showed that the average concentrations of PM2.5, PM10, and NO2 ranged from 8.8 to 310.7, 20.1–515.8, and 40.0–123.7 μg/m3, respectively, during the dry season. In the wet season, the ranges for PM2.5, PM10, NO2, and CO levels were 17.2–117.4, 24.3–167.2, 31.8–111.3, and 77–33312 μg/m3, respectively. The wide range of variations in the concentrations of PM2.5, PM10, NO2 and CO variations may be related to spatial and temporal factors such as sampling locations and seasonal changes. In both the wet and dry seasons, the hazard quotient (HQ) for PM2.5 and PM10 was greater than one, suggesting a non-carcinogenic effect. The PM2.5 excess lifetime cancer risk (ELCR) ranged from 0.1 to 0.7, which is greater than the recommended values by the WHO (ranging from 1 × 10−5 to 1 × 10−6) and the USEPA (less than 1 × 10−6). The HQ and ELCR values imply a considerable health risk for the general population.

Development of High-Precision NO2 Gas Sensor Based on Non-Dispersive Infrared Technology.

Increasing concerns about air quality due to fossil fuel combustion, especially nitrogen oxides (NOx) from marine and diesel engines, necessitate advanced monitoring systems due to the significant health and environmental impacts of nitrogen dioxide (NO2). In this study, a gas detection system based on the principle of the non-dispersive infrared (NDIR) technique is proposed. Firstly, the pyroelectric detector was developed by employing an ultra-thin LiTaO3 (LT) layer as the sensitive element, integrated with nanoscale carbon material prepared by wafer-level graphics technology as the infrared absorption layer. Then, the sensor was hermetically sealed using inert gas through energy storage welding technology, exhibiting a high detectivity (D*) value of 4.19 × 108 cm·√Hz/W. Subsequently, a NO2 gas sensor was engineered based on the NDIR principle employing a Micro Electro Mechanical System (MEMS) infrared (IR) emitter, featuring a light path chamber length of 1.5 m, along with integrated signal processing and software calibration algorithms. This gas sensor was capable of detecting NO2 concentrations within the range of 0-500 ppm. Initial tests indicated that the gas sensor exhibited a full-scale relative error of less than 0.46%, a limit of 2.8 ppm, a linearity of -1.09%, a repeatability of 0.47% at a concentration of 500 ppm, and a stability of 2% at a concentration of 500 ppm. The developed gas sensor demonstrated significant potential for application in areas such as industrial monitoring and analytical instrumentation.

Excess deaths and loss of life expectancy attributed to long-term NO2 exposure in the Chinese elderly

BackgroundEvidence linking nitrogen dioxide (NO2) air pollution to life span of high-vulnerability older adults is extensively scarce in low- and middle-income countries. This study seeks to quantify mortality risk, excess deaths, and loss of life expectancy (LLE) associated with long-term exposure to NO2 among elderly individuals in China. MethodsA nationwide dynamic cohort of 20352 respondents ≥65 years old were enrolled from the China Longitudinal Health and Longevity Survey during 2005–2018. Residential exposures to NO2 and co-pollutants were assessed by well-validated spatiotemporal prediction models. A Cox regression model with time-dependent covariates was utilized to quantify the association of all-cause mortality with NO2 exposure, controlling for confounders such as demographics, lifestyle, health status, and ambient temperature. NO2-attributable deaths and LLE were evaluated for the years 2010 and 2020 based on the pooled NO2-mortality relation derived from multi-national cohort investigations. Decomposition analyses were conducted to dissociate net shift in NO2-related deaths between 2010 and 2020 into four primary contributing factors. ResultsA total of 14313 deaths were recorded during follow-up of approximately 100 hundred person-years (median 3.6 years). We observed an approximately linear relationship (nonlinear P = 0.882) of NO2 exposure with all-cause death across a broad range from 6.6 to 95.7 μg/m3. Every 10-μg/m3 rise in yearly average NO2 concentration was linked to a hazard ratio (HR) of 1.045 (95% confidence interval [CI]: 1.031–1.059). In the updated meta-analysis of this study and 9 existing cohorts, we estimated a pooled HR of 1.043 (95% CI: 1.023–1.063) for each 10-μg/m3 growth in NO2. Reaching a 10-μg/m3 counterfactual target of NO2 concentration in China could avoid 0.33 (95% empirical CI: 0.19–0.49) million premature deaths and an LLE of 0.40 (95% empirical CI: 0.23–0.59) years in 2010, which greatly dropped to 0.24 (95% empirical CI: 0.14–0.36) million deaths and 0.21 (95% empirical CI: 0.12–0.31) years of LLE in 2020. The net fall in NO2-attributable deaths (–26.8%) between 2010 and 2020 was primarily driven by the declines in both NO2 concentration (–41.6%) and mortality rate (–27.1%) under population growth (+41.0%) and age structure transition (+0.9%). ConclusionsOur findings provide national evidence for increased risk of premature death and loss of life expectancy attributed to later-life NO2 exposure among the elderly in China. In an accelerated aging society, strengthened clean air actions should be formulated to minimize the health burden and regional inequality in NO2-attributable mortality.

Particulate and gaseous air pollutants exceed WHO guideline values and have the potential to damage human health in Faisalabad, Metropolitan, Pakistan

First-ever measurements of particulate matter (PM2.5, PM10, and TSP) along with gaseous pollutants (CO, NO2, and SO2) were performed from June 2019 to April 2020 in Faisalabad, Metropolitan, Pakistan, to assess their seasonal variations; Summer 2019, Autumn 2019, Winter 2019–2020, and Spring 2020. Pollutant measurements were carried out at 30 locations with a 3-km grid distance from the Sitara Chemical Industry in District Faisalabad to Bhianwala, Sargodha Road, Tehsil Lalian, District Chiniot. ArcGIS 10.8 was used to interpolate pollutant concentrations using the inverse distance weightage method. PM2.5, PM10, and TSP concentrations were highest in summer, and lowest in autumn or winter. CO, NO2, and SO2 concentrations were highest in summer or spring and lowest in winter. Seasonal average NO2 and SO2 concentrations exceeded WHO annual air quality guide values. For all 4 seasons, some sites had better air quality than others. Even in these cleaner sites air quality index (AQI) was unhealthy for sensitive groups and the less good sites showed Very critical AQI (> 500). Dust-bound carbon and sulfur contents were higher in spring (64 mg g−1) and summer (1.17 mg g−1) and lower in autumn (55 mg g−1) and winter (1.08 mg g−1). Venous blood analysis of 20 individuals showed cadmium and lead concentrations higher than WHO permissible limits. Those individuals exposed to direct roadside pollution for longer periods because of their occupation tended to show higher Pb and Cd blood concentrations. It is concluded that air quality along the roadside is extremely poor and potentially damaging to the health of exposed workers.Graphical abstract

QUANTIFYING THE IMPACT OF TRAFFIC-RELATED EMISSIONS ON AIR QUALITY IN KANO CITY, NIGERIA

The aim of this study is to assess the concentration of traffic related air pollutants at some major traffic junctions in Kano metropolis, it comprised of carrying traffic count in the vicinities so as ascertain the sources of the pollution and identify the level of carbon monoxide (CO), nitrogen dioxide (NO 2), Sulphur dioxide (SO 2) for a period of six weeks. Traffic density was estimated via manual counting using tally sheet system. Crowcon gasman to air samplers ware used to measure the concentrations of gases emitted into the atmosphere at the selected locations. A total of 126 samples of air quality measurements were taken out for three hours, with each hour devoted to morning, afternoon and evening peak periods. Results obtained were compared with National Environmental Standard and Regulatory Enforcement Agency (NESREA) guideline limits. Data were analysed using statistical package IBM SPSS version 16.0 to compute descriptive statistics, Pearson correlation, regression analysis and one sample t-test. There was a correlation between CO, NO 2, SO 2 and traffic volume in the entire junctions, and most of the correlation obtained may be attributed to the high vehicular volume in the traffic jam. The result of this indicated that the mean concentration of CO, NO 2 and SO 2 emission at Kabuga, Kofar ruwa, Hotoro NNPC and Aminu Kano Teaching Hospital traffic junctions, gaseous emissions were within NESREA guideline limit for ambient conditions, except for CO concentration of 14.43 and 13.60 ppm at Bata and Kantin Kwari intersections were found to be much higher when compared with the NESREA guideline limits, respectively. This implies that transport-related pollution around Bata and Kantin Kwari is apparently unsafe. Similarly, it is likely that air quality will continue to deteriorate as the city continues to grow. Therefore, there is need for government to institute and enforce traffic emission control and air quality management programs as a priority. They should also work to prevent further environmental degradation by adopting effective policy in Nigeria.

Diurnal variation of indoor air pollutants and their influencing factors in educational buildings: A case study using LASSO-based ANNs

This study explores the diurnal variations and influencing factors of PM2.5, NO2, and ozone concentrations in educational buildings. Utilizing an integrated system of indoor and outdoor sensors, building automation control networks, and walk-through inspections, air quality data along with relevant characteristics were collected from ten educational buildings in Central Florida. Advanced Neural Network models (RNNs and CNNs), including the Long Short-Term Memory (LSTM) and the Attention Temporal Convolutional Network (ATCN) algorithms based on the Least Absolute Shrinkage and Selection Operator (LASSO), were developed to accurately identify diurnal patterns in indoor air quality (IAQ) and the differences in influencing factors. The findings indicate greater variability in diurnal differences and factors influencing indoor NO2 and ozone concentrations compared to PM2.5. Although the factors influencing day and night PM2.5 levels were similar, there were significant differences in the contribution weights of these factors. Optimized RNNs and CNNs significantly outperformed standard Artificial Neural Network (ANN) models in dynamically simulating and predicting target pollutants. Comparative analysis of the root-mean-square error (RMSE) demonstrated that LASSO-LSTM models comprehensively outperformed LASSO-ATCN models by averaging 13.4% (p < 0.05). These results can be referenced in studies concerning Indoor Air Quality (IAQ) control conducted in similar environmental settings.

Theoretical study on noninvasive detection of COPD disease exhaled gas molecules using N doped tellurene

Medical studies have shown that the concentration of NO2 exhaled by patients with obstructive pneumonia is significantly higher than that of healthy individuals. This study employs density functional theory (DFT) calculations to theoretically investigate the sensing performance of N or Pt elements doped tellurene structures for NO2 detection. Promising rapid early screening for this disease. The results indicate that doping enhances the electrical performance of tellurene structures, and N doping demonstrates superior advantages. Based on the changes in bandgap before and after gas adsorption, which directly affect the conductivity, a significant change in the electrical signal can be generated, providing sufficient detectable electrical response. Simultaneously, based on the N–Te doping structure and the interaction with NO2 gas, it is physical adsorption. Increasing the temperature can accelerate the desorption of gas molecules, which is beneficial for the reuse of the detection material. The research findings indicate excellent selectivity of NO2 gas over interfering gases (N2, O2, H2O, and CO2) during competitive adsorption. Furthermore, the N–Te doped structure significantly improves the adsorption efficiency for NO2, with an adsorption energy of −1.74 eV and charge transfer of −0.37 e. Compared to the previously reported SnSe, SnP3, and PdSe2, the N–Te doped structure exhibits significant advantages. These results suggest that the N–Te doped structure has the potential to become a reliable sensing material for NO2, with significant application potential in noninvasive and rapid detection of obstructive pneumonia.

Optimizing removal of elemental mercury from flue gas using halide‐impregnated red mud

AbstractMercury (Hg0) emission from coal‐fired industrial plants poses severe threats to ecosystem sustainability and human health, urging the development of novel and cost‐effective adsorbents to treat industrial flue gas. Herein, the modification of industrial residual red mud (RM) through the impregnation of hydrogen halides (HH) and its adsorption characteristics for removing elemental mercury from combustion flue gas was reported. Experimental investigation of HH‐modified RM reveals that the hydrogen iodide (HI)‐modified RM with a concentration of 1.5 M had a maximum Hg0 removal efficiency of 98%, whereas hydrogen bromide (HBr) 1.5 M modified RM had a maximum Hg0 removal efficiency of 90%. The effect of various parameters, such as reaction temperature and halide concentrations, were also found to be influential for the adsorption efficiency of the modified RM. Moreover, it is important to highlight the chemisorption characteristics of HI‐modified RM, which significantly enhances the efficiency of the removal process. The Hg0 removal efficiency increases with the increase in HI concentration (1.5 M @ 93%) with an optimal reaction temperature of 140°C. Furthermore, the maximum Hg0 removal attained with a change in NO concentration was 98% at 200 ppm. However, increasing the SO2 concentration reduces the efficiency of RM for removing Hg0 in simulated coal combustion flue gas. The pseudo‐second‐order model (R2 = 0.98) accurately describes the adsorption of in kinetic investigations, indicating a chemisorption mechanism. This analysis of the chemisorption mechanism highlights the efficiency of halide‐modified industrial solid waste, which has the potential to be used in the design of economical and innovative adsorbents for reducing environmental pollution. The present study employed specific reaction parameters such as reaction temperature, halide loading contents, and different flue gas compositions, which had not been extensively explored.

Soil Moisture, Soil NOx and Regional Air Quality in the Agricultural Central United States

AbstractAgricultural soils containing nitrogen‐rich fertilizers are a substantial source of reactive nitrogen to the atmosphere with potential to impact air quality. One form of reactive nitrogen, nitrogen oxides (NOx = NO + NO2), are a harmful air pollutant and form secondary pollutants, including particulate matter (PM) and ozone (O3). Soil nitrogen oxide emissions (SNOx) are heavily influenced by environmental conditions, however the understanding of the influence of environmental drivers on the behavior of SNOx is limited. Here, we implement a modified soil moisture‐dependent SNOx parameterization into the Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) and investigate the impact on regional air quality in the central U.S. Evaluating against TROPOspheric Monitoring Instrument (TROPOMI) column NO2 observations, WRF‐Chem columns better capture the TROPOMI column magnitudes earlier in the growing season when using the updated SNOx parametrization, with modeled column bias improved to −1.1% over the most heavily fertilized regions. Evaluating against Environmental Protection Agency (EPA) surface NO2 observations, the relationship between surface NO2 and soil moisture is better represented in agriculturally‐dominant regions when using the updated parameterization, with greatest surface NO2 concentrations at moderate soil moisture and lower concentrations at wetter or drier soil conditions. In simulations, these SNOx lead to increased O3 in select urban regions, with more than double the occurrences of O3 exceeding the EPA 8‐hr O3 standard of 70 ppb.

Maritime sector contributions on NO2 surface concentrations in major ports of the Mediterranean Basin

The aim of this study is to estimate the contribution of the maritime sector on the air quality of major Mediterranean ports with the Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) chemical transport model, using two source apportionment methods: the brute-force emission scenario methodology and the labelling approach. With the brute-force method NO2 shipping concentrations are estimated by the ratio of the difference between two model runs - with original and reduced emissions - and the equivalent emission reduction factor. In the labelling, emitted species are explicitly labelled per sector and tracked through all model processes. Simulations are performed for a one-year period, from May 2018 to May 2019 and the two methodologies are intercompared. The methods show strong agreement between NO2 shipping contributions (R > 0.95) and differences of ∼14%. A sensitivity analysis carried out using the brute-force method indicates that a linear regime between NOX emissions and NO2 concentrations holds, when adopting a low to moderate emission reduction (<50%). We applied the brute-force method with an assumed 20% emission reduction and found that the NO2 surface concentrations attributed to maritime sector activities in selected ports were between 5% and 70% of the total NO2 surface concentrations, with a mean value of 27%. Comparisons between measurements from the European Environment Agency (EEA) ground-based monitoring stations and LOTOS-EUROS NO2 surface concentrations show a strong correlation (R∼0.8) with an underestimation of the model (∼-32%) for the whole period. The bias is reduced to −20% when air quality monitoring stations affected by traffic and industrial activities are excluded from the analysis. Moreover, observed Sentinel-5 Precursor TroPospheric MOnitoring Instrument (S5P/TROPOMI) and modelled NO2 vertical column densities (VCDs) show a significant spatial agreement (R∼0.86) for both summer and winter with biases of −25% and −1%, respectively, over the selected ports. These comparisons were carried out as an indirect way of validating the applied methodology and the performance of the model in coastal areas. The present study provides a solid background which will enable the assimilation of the satellite observations to the CTM to infer NOX shipping emissions in the Mediterranean Basin in view of the upcoming designation of the Mediterranean Sea as an Emission Control Area in 2025.

Hydrochars derived from real organic wastes as carbonaceous precursors of activated carbons for the removal of NO from contaminated gas streams

The improvement of air quality in densely-populated urban regions constitutes an environmental challenge of increasing concern. In this respect, the abatement of NO emissions, primarily emanating from combustion processes associated with motor-vehicles, along with industrial/domestic combustion systems, represents one of the main problems. Here, three hydrochars from diverse organic residues were used as activated carbon precursors for their evaluation in the NO removal in two potential application scenarios. Hydrochars were physically activated at 800 °C with pure-CO2 or diluted-O2. These materials were tested in a lab-scale biofilter at different conditions (NO concentration, temperature, relative humidity, NO-containing gas and carbon particle size) and in a larger-scale biofilter to evaluate the long-term NO removal capacity. Hydrochar-derived carbons present a relatively well-developed micro- and mesoporous structure, with BET areas of up to 421 m2/g, and a variety of oxygen surface functionalities (carboxylic, lactone, carbonyl and quinone groups), especially concerning CO2-activated carbons. These exhibited an excellent behaviour at low NO concentration (5 ppmv) between 25 and 75 °C with removal capacities of ≈97 % and > 82 %, respectively; and still good-performance (≈66 %) in a more concentrated gas (120 ppmv). Whilst, carbons obtained by diluted-O2 activation from the same hydrochars, evidenced a higher removal capacity loss at high NO concentration. The O2 presence in the gas stream was confirmed as a crucial factor in the NO elimination, since both co-adsorb on the carbon surface favouring NO oxidation to NO2. Besides, the humidity in the airstream diminished the NO removal capacity from 0.88 to 0.51 mgNO/gcarbon, but still remained at 0.54 mgNO/gcarbon, when the carbon (in pellet) was operated at larger-scale biofilter in 9-fold longer test under humid air. Therefore, this study highlights the potential of renewable carbons to serve as cost-effective component in urban biofilters, to mitigate NO emissions from exhaust gases in biomass boilers and urban semi-close areas.

Identifying Driving Factors of Atmospheric N2O5 with Machine Learning.

Dinitrogen pentoxide (N2O5) plays an essential role in tropospheric chemistry, serving as a nocturnal reservoir of reactive nitrogen and significantly promoting nitrate formations. However, identifying key environmental drivers of N2O5 formation remains challenging using traditional statistical methods, impeding effective emission control measures to mitigate NOx-induced air pollution. Here, we adopted machine learning assisted by steady-state analysis to elucidate the driving factors of N2O5 before and during the 2022 Winter Olympics (WO) in Beijing. Higher N2O5 concentrations were observed during the WO period compared to the Pre-Winter-Olympics (Pre-WO) period. The machine learning model accurately reproduced ambient N2O5 concentrations and showed that ozone (O3), nitrogen dioxide (NO2), and relative humidity (RH) were the most important driving factors of N2O5. Compared to the Pre-WO period, the variation in trace gases (i.e., NO2 and O3) along with the reduced N2O5 uptake coefficient was the main reason for higher N2O5 levels during the WO period. By predicting N2O5 under various control scenarios of NOx and calculating the nitrate formation potential from N2O5 uptake, we found that the progressive reduction of nitrogen oxides initially increases the nitrate formation potential before further decreasing it. The threshold of NOx was approximately 13 ppbv, below which NOx reduction effectively reduced the level of night-time nitrate formations. These results demonstrate the capacity of machine learning to provide insights into understanding atmospheric nitrogen chemistry and highlight the necessity of more stringent emission control of NOx to mitigate haze pollution.

Polar tri-glycol side chain for enhanced detection to low concentrations of NO2 at room temperature

It has been widely studied the impact of polar side-chain on the modification of conjugated polymers and the thermoelectric properties of composites, but relatively little research has been carried out in the gas sensing field. In this study, two π-conjugated polymers PFTC8 and PFTO with similar backbone but different polar-side chains were synthesized. The separation efficiency of the polymers for semiconducting single-walled (sc-SWCNTs) and the NO2 sensing performance were also investigated. It was found that the introduction of the polar side chains not only enhanced the sorting efficiency of the polymer for sc-SWCNTs (greater than 99 %), but also further improved the responsive performance of the sensor to NO2. After the introduction of polar side chains, the response value of the composite PFTO@sc-SWCNTs towards 1 ppm NO2 was achieved to be 21.7 % at room temperature. Furthermore, the response time (Tres = 23 s) and recovery time (Trec = 134 s) of PFTO@sc-SWCNTs to 1 ppm NO2 were also greatly shortened. The strategy for improving the NO2 gas response performance by introducing polar side chains is proposed.

Weather-resistant wood for sound absorption, thermal insulation and NO removal

Noise and nitrogen oxide (NOx) pollution are harmful to human health, and using sustainable materials such as discarded wood to fabricate insulating building materials can address the issues of environmental pollution and energy crisis. Thus, fabricating multifunction materials with discarded wood by advanced technology can bring exciting innovation. In this study, a novel “weather-resistant wood” with superior weathering resistance, sound absorption, thermal insulation, and photocatalytic properties is reported. The weather-resistant wood was prepared using a simple high-temperature alkali treatment method that allowed the introduction of alkali metal-doped and N-vacancy rich C3N4 onto the wood. The prepared weather-resistant wood sample (with a thickness of 10 mm) exhibited a high porosity of 89.6 %, noise-reduction coefficient of 0.28, and thermal conductivity of 0.0586 W m−1 K−1. Moreover, it could remove 45.4 % of ultralow concentrations of NO (∼500 ppb) under visible-light irradiation. The developed weather-resistant wood demonstrates considerable potential for applications in our lives including noise reduction and improvements in air quality.

Dominant mechanism underlying the explosive growth of summer surface O3 concentrations in the Beijing-Tianjin-Hebei Region, China

Despite the implementation of stringent emission reduction measures in China since 2013, ozone (O3) pollution remains a serious concern. Many O3 pollution processes are accompanied by the explosive growth of O3 concentration, meanwhile this explosive growth will trigger O3 pollution with a high probability. This study investigates the chemical and physical mechanisms behind the explosive growth of O3 in the summer within the Beijing-Tianjin-Hebei (BTH) region based on the atmospheric circulation clustering and WRF-CMAQ simulation. Ninety-four instances of O3 explosions from the summer of 2014–2021 were selected based on their day-to-day variation in O3 concentration, and the corresponding meteorological conditions were classified into four clusters using K-means analysis. Cluster 1 revealed that large-scale regional transmission of O3 and its precursors from the southern BTH region led to a renewed burst of O3 growth based on mild pollution. Nighttime reduction in NO2 concentration decreases titration consumption, causing a nocturnal rise in O3 concentration, detectable through observed data and progress analysis. In Cluster 2 and Cluster 3, the BTH region is located in the northeast wind area ahead of the ridge and northly wind area of post-trough situation, respectively. The vertical downward motion with the markable increase in surface solar radiation (SSR), 2-m temperature (T2M), and decrease in relative humidity (RH) contribute to photochemical O3 generation. Additionally, enhancements in boundary layer stability and reductions in dry deposition and vertical diffusion are favorable for the O3 accumulation. Cluster 4 showed the explosive growth after rainfall event, with the largest increment. The post-rain increase in SSR and T2M, coupled with decreased RH, promotes photochemical O3 formation. Moreover, progress analysis reveals the reduced cloud cover slows down O3 removal, contributing to O3 accumulation. The dominant mechanism behind the explosive growth of O3 will provide certain guidance for the prediction of O3.

Local climate, air quality and leaf litter cover shape foliar fungal communities on an urban tree

Foliar fungi on urban trees are important for tree health, biodiversity and ecosystem functioning. Yet, we lack insights into how urbanization influences foliar fungal communities. We created detailed maps of Stockholm region’s climate and air quality and characterized foliar fungi from mature oaks (Quercus robur) across climatic, air quality and local habitat gradients. Fungal richness was higher in locations with high growing season relative humidity, and fungal community composition was structured by growing season maximum temperature, NO2 concentration and leaf litter cover. The relative abundance of mycoparasites and endophytes increased with temperature. The relative abundance of pathogens was lowest with high concentrations of NO2 and particulate matter (PM2.5), while saprotrophs increased with leaf litter cover. Our findings show that urbanization influences foliar fungi, providing insights for developing management guidelines to promote tree health, prevent disease outbreaks and maintain biodiversity within urban landscapes.