NO2 Monitoring Research Articles

Development and Field Deployment of a ppb-Level SO2/NO2 Dual-Gas Sensor System for Agricultural Early Fire Identification.

Sulfur dioxide (SO2) and nitrogen dioxide (NO2) are chemical indicators of crop straw combustion as well as significant atmospheric pollutants. It is challenging to promptly detect natural "wildfires" during agricultural production, which often lead to uncontrollable and substantial economic losses. Moreover, both "wildfires" and artificial "straw burning" practices pose severe threats to the ecological environment and human health. Consequently, developing sensors capable of rapid and high-precision quantitative analysis of SO2/NO2 is essential and urgent for detecting early fires in agricultural activities. Here, we demonstrate an incoherent broadband cavity-enhanced absorption spectroscopy (IBBCEAS) sensing system utilizing a 366 nm ultraviolet light emitting diode, designed for real-time, high-precision monitoring of SO2 and NO2 and is used for early fire detection validation. The optical resonant cavity is constructed within a 60 mm cage system mechanical structure, achieving a maximum optical path length of nearly 2 km with a length of ∼460 mm. The output light carrying information about the species and concentration of the analyte molecules is coupled into the miniaturized grating spectrometer via a fiber, and continuous spectral fitting and concentration inversion are performed on the computer. We propose a spectral analysis and concentration inversion model based on an improved particle swarm optimization-support vector machine (IPSO-SVM) algorithm. By discrimination of the absorption spectral characteristics of SO2/NO2, we achieve superior prediction accuracy. Experimental results indicate that the detection limits of SO2 and NO2 under the optimized averaging time are 77.5 parts per billion by volume (ppbv) and 0.037 ppbv, respectively. The field deployment of the sensor in scenarios such as continuous outdoor air pollution monitoring, in situ combustion feature identification, and early fire mobile detection has demonstrated the superior reliability and sensitivity of this sensor system.

Waste loofah sponge-derived graphitic carbon/ZnO tubules enabling ultrahigh response to NO2 gas at near room temperature

The fabrication of low-temperature and ultrahigh-response ZnO-based sensor is very important for real-time and effective monitoring of harmful NO2, but the less association has been reported to date. Herein, based on the concept of biomimetic manufacturing and waste reutilization, two biomorphic ZnO tubules were controllably prepared by calcining zinc nitrate solution immersed precursors in air using loofah sponge as template. Among them, the GC/ZnO tubules obtained from at 425 °C are composited of 5.26 wt% graphitic carbon (GC) and uniform nanoparticles, featuring small-size mesopores, large specific surface area, and abundant oxygen vacancies. Such advantageous features facilitate the rapid diffusion of target gases, increase the conductivity of sensing material, readily expose more active sensing units and promote surface chemical reactions, thereby effectively boosting the sensitive and selective detection of oxidizing NO2 gas. At near room temperature of 50 °C, GC/ZnO sensor achieves response value of 912 toward 5 ppm NO2 gas, which is 3.14 times higher than that of pure ZnO-500, and is the largest value among reported carbon-doped ZnO-based sensors. Additionally, it also exhibits other good comprehensive performance. Therefore, the cost-effective GC/ZnO tubules can serve as candidate material for accurately detecting NO2 gas in the atmospheric environment.

(Invited) ppb-Level NO2 Monitoring Using Lung-Inspired MOF on Graphene Hybrids Sensor

NO2 is associated with 1.8% of all cardiovascular mortality and even the progression of neurodegenerative diseases1. To safeguard against this threat, the WHO has defined a NO2 exposure limit at ~5 ppb in its Global Air Quality Guidelines2. Consequently, it is essential to continuously and accurately monitor NO2 at ppb levels to provide personalized pollutant data. Ideal NO2 sensors should fulfil specific requirements: the ability to detect ppb-level NO2 in real-time, simplicity in operation (without external thermal- or photo excitation), cost efficiency, and wearability (lightness and flexibility). However, conventional materials such as metal oxides, transition metal dichalcogenides, or carbon derivatives often fail to meet these criteria due to their low sensitivity, slow response/recovery time, and high operating temperature3.Metal-organic frameworks (MOFs), comprising inorganic building units coordinated with organic linkers, are compelling opportunities to develop highly sensitive gas sensors. With their customizable topology, exceptional surface area, and uniform nanopore distribution4, MOFs are becoming increasingly attractive for their use in electrical sensory platforms owing to superior gas adsorption and surface reaction capabilities. Specifically, the semiconducting Cu3HHTP2 MOF has shown superior sensitivity and selectivity toward NO2 due to its strong redox activity. Nevertheless, practical challenges remained with using MOFs for NO2 sensors, such as limited mass flow, fabricating technology, and flexibility.In this work, we propose the use of laser-induced graphene (LIG) as a suitable platform for growing Cu3HHTP2 MOF to facilitate real-time monitoring of ppb-level NO2. LIG, an emerging 3D macroporous material, can be produced via direct laser irradiation of various polymer substrates5. By combining Cu3HHTP2 and LIG (resulting in LIG@Cu3HHTP2), we have observed synergistic effects that are not achievable with MOF alone (Fig. 1-4). Firstly, the nanostructured MOFs synthesized on the LIG platform accelerated the mass transport of exposed gases, which is attributable to their hierarchical macro-/microporous architecture. Moreover, the expanded exposure surface area leveraged the intrinsic advantage of MOFs, characterized by their abundant open metal sites and edge ligands for molecular adsorption. The human lung is an excellent example of a hierarchical pore architecture, enabling rapid transport of gas molecules over a large surface area. The LIG@Cu3HHTP2 showcased one of the most rapid response/recovery times (16 s/15 s) and the lowest limit of detection (LoD, 0.168 ppb) amongst cutting-edge NO2 sensors operating at room temperature and atmospheric conditions (Fig. 5-8). In addition, the selectivity toward NO2 over interference gases was demonstrated as a result of charge carrier transfer and size discrimination effect of MOF (Fig. 9). Secondly, we authenticated a patterning strategy for solution-based MOF growth, addressing one of the pivotal challenges in fabricating MOF-based electronic devices (Fig. 4). The LIG provides plentiful defect sites and dangling hydroxyl functional groups, forming an optimal platform for MOF nucleation. Also, in terms of device fabrication, laser processing can enable rapid, energy-efficient programmable micropatterning. Lastly, whereas traditional MOF-based electronic devices have predominantly been restricted to rigid substrates, our approach enabled their integration onto flexible and lightweight substrates via formation on LIG (Fig. 10 and 11). The flexibility of the resultant sensor was evaluated through computational modelling and real cyclic bending tests, with resilience observed across 10,000 iterative bending cycles, even under a stringent 2.5 mm curvature radius.Consequently, we successfully developed a LIG@Cu3HHTP2 hybrid capable of monitoring NO2 at the ppb level in real-time. These results will inform the advancement of MOFtronics applications, currently confined to lab-scale explorations, facilitating their transition towards real-world implementation as high-efficiency sensors.This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Ministry of Education (2020R1A6A1A03040516). A. Schneider, et al., Umweltbundesamt, 2018.WHO, Global Air Quality Guidelines, 2021.S. Kumar, et al., Mater. Sci. Semicond. Process., 104865, 2020.M. D. Allendorf, et al., Chem. Rev., 120, 2020.F. M. Vivaldi, et al., ACS Appl. Mater. Interfaces. 13(26), 2021. Figure 1

Enhancing the Reliability of NO2 Monitoring Using Low-Cost Sensors by Compensating for Temperature and Humidity Effects

The study investigates methods to enhance the reliability of NO2 monitoring using low-cost electrochemical sensors to measure gaseous pollutants in air by addressing the impacts of temperature and relative humidity. The temperature within a plastic container was controlled using an internal mica heater, an external hot air blower, or cooling packs, while relative humidity was adjusted using glycerine solutions. Findings indicated that the auxiliary electrode signal is susceptible to temperature and moderately affected by relative humidity. In contrast, the working electrode signal is less affected by temperature and relative humidity; however, adjustments are still required to determine gas concentrations accurately. Tests involving on/off cycles showed that the auxiliary electrode signal experiences exponential decay before stabilizing, requiring the exclusion of initial readings during monitoring activities. Additionally, calibration experiments in zero air allowed the determination of the compensation factor nT across different temperatures and humidity levels. These results highlight the importance of compensating for temperature and humidity effects to improve the accuracy and reliability of NO2 measurements using low-cost electrochemical sensors. This refinement makes the calibration applicable across a broader range of environmental conditions. However, the experiments also show a lack of repeatability in the zero air calibration.

Development and Performance of ZnO/MoS2 Gas Sensors for NO2 Monitoring and Protection in Library Environments

The presence of harmful oxidizing gases accelerates the oxidation of cellulose fibers in paper, resulting in reduced strength and fading ink. Therefore, the development of highly sensitive NO2 gas sensors for monitoring and protecting books holds significant practical value. In this manuscript, ZnO/MoS2 composites were synthesized using sodium molybdate and thiourea as raw materials through a hydrothermal method. The morphology and microstructure were characterized by X-ray diffraction analysis (XRD), energy dispersive spectroscopy (EDS), field emission scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The ZnO/MoS2 composite exhibited a flower-like structure, with ZnO nanoparticles uniformly attached to the surface of MoS2, demonstrating advantages such as high specific surface area and good uniformity. The gas sensitivity of the ZnO/MoS2 nanocomposites reached its peak at 260 °C, with a sensitivity value around 3.5, which represents an improvement compared to pure ZnO, while also enhancing sensitivity. The resistance of the ZnO/MoS2 gas sensor remained relatively stable in air, exhibiting short response times during transitions between air and NO2 environments while consistently returning to a stable state. In addition to increasing adsorption capacity and improving light utilization efficiency, the formation of hetero-junctions at the ZnO-MoS2 interface creates an internal electric field that effectively promotes the rapid separation of photo-generated charge carriers within ZnO, thereby extending carrier lifetime.

High-performance machine-learning-based calibration of low-cost nitrogen dioxide sensor using environmental parameter differentials and global data scaling

Accurate tracking of harmful gas concentrations is essential to swiftly and effectively execute measures that mitigate the risks linked to air pollution, specifically in reducing its impact on living conditions, the environment, and the economy. One such prevalent pollutant in urban settings is nitrogen dioxide (NO2), generated from the combustion of fossil fuels in car engines, commercial manufacturing, and food processing. Its elevated levels have adverse effects on the human respiratory system, exacerbating asthma and potentially causing various lung diseases. However, precise monitoring of NO2 requires intricate and costly equipment, prompting the need for more affordable yet dependable alternatives. This paper introduces a new method for reliably calibrating cost-effective NO2 sensors by integrating machine learning with neural network surrogates, global data scaling, and an expanded set of correction model inputs. These inputs encompass differentials of environmental parameters (such as temperature, humidity, atmospheric pressure), as well as readings from both primary and supplementary low-cost NO2 detectors. The methodology was showcased using a purpose-built platform housing NO2 and environmental sensors, electronic control units, drivers, and a wireless communication module for data transmission. Comparative experiments utilized NO2 data acquired during a five-month measurement campaign in Gdansk, Poland, from three independent high-precision reference stations, and low-cost sensor data gathered by the portable measurement platforms at the same locations. The numerical experiments have been carried out using several calibration scenarios using various sets of calibration input, as well as enabling/disabling the use of differentials, global data scaling, and NO2 readings from the primary sensor. The results validate the remarkable correction quality, exhibiting a correlation coefficient exceeding 0.9 concerning reference data, with a root mean squared error below 3.2 µg/m3. This level of performance positions the calibrated sensor as a dependable and cost-effective alternative to expensive stationary equipment for NO2 monitoring.

Flexible and highly selective NO2 gas sensor based on direct-ink-writing of eco-friendly graphene oxide for smart wearable application

Nitrogen dioxide (NO2) is a major cause of respiratory disorders in outdoor and indoor environments. Real-time NO2 monitoring using nonintrusive wearable devices can save lives and provide valuable health data. This study reports a room-temperature, wearable, and flexible smart NO2 gas sensor fabricated via cost-effective printing technology on a polyimide substrate. The sensor uses alkali lignin with edge-oxidised graphene oxide (EGO-AL) ink, demonstrating a sensitivity of 1.70% ppm⁻1 and a detection limit of 12.70 ppb, with excellent selectivity towards NO2. The high sensing properties are attributed to labile oxygen functional groups from GO and alkali lignin, offering abundant interacting sites for NO2 adsorption and electron transfer. The sensor fully recovers to the baseline after heat treatment at 150 °C, indicating its reusability. Integration into lab coats showcased its wearable application, utilising a flexible printed circuit board to wirelessly alert the wearer via cell phone to harmful NO2 levels (>3 ppm) in the environment. This smart sensing application underscores the potential for practical, real-time air quality monitoring, personal safety enhancement, and health management.

Fabrication and application of ZIF-8 derived In2O3-ZnO nanocomposites for ultra-sensitive NO2 sensing under UV irradiation at room temperature

Nitrogen oxide (NO2) is common by-products of industrial production, posing significant health risks to workers. Therefore, real-time monitoring of NO2 is crucial. In this study, innovative In2O3-ZnO nano-materials were prepared by combining In2O3 with ZIF-8 via electrospinning, for detecting trace amounts of NO2 at room temperature (RT). Compared to pure In2O3, the In2O3-ZnO nano-materials (V2) with a 10 % weight ratio of ZIF-8 exhibited a significantly higher response (389.99) and a faster response time (16.9 s) to NO2 under ultraviolet (UV) irradiation at RT. The superior gas sensing performance of the V2 nano-materials is attributed to the formation of n-n heterojunctions, enhanced optical absorption, increased oxygen vacancy ratio, and excellent dispersion among the nano-materials. The results demonstrate that V2 nano-materials can detect NO2 at parts-per-billion (ppb) levels at RT, making them suitable for monitoring trace amounts of NO2 in open environments. Furthermore, a portable real-time NO2 monitoring device was constructed using V2 as the sensing material, an LM358-based amplifier circuit for data acquisition, an STM32F103C8T6 microcontroller as the main control unit, and a buzzer as an alarm device. Compared to devices using a helical Ni-Cr wire as an indirect heat source, this integrated NO2 detection device offers real-time alarms with lower energy consumption. This application not only enhances worker health and safety in industrial environments but also demonstrates higher energy efficiency in practical application.

Daily NO2 Simulation Research Based on Automatic Machine Learning Ensemble Models

To understand the spatial distribution of NO2 near the surface, we utilized measured data from NO2 monitoring stations and combined it with column concentration data from the Tropospheric Monitoring Instrument （TROPOMI）, taking the Yangtze River Delta region as the study area. We considered the impact of factors such as population, elevation, and meteorological conditions on NO2 levels. We used automated machine learning to select five machine-learning algorithms with high simulation accuracy, namely ET, RF, XGBoost, LightGBM, and Catboost, and then integrated these five algorithms using the Stacking model to simulate the daily NO2 concentration in the Yangtze River Delta region from March 2020 to February 2021. The results indicated that the RMAE and MAE values of the Stacking ensemble model were 7.078 and 5.270, respectively, which outperformed the single algorithms of ET, RF, XGBoost, LightGBM, and Catboost. The spatial distribution of high NO2 concentrations in the Yangtze River Delta region, consisting of three provinces and one municipality, exhibited a U-shaped pattern with the convergence point located at the intersection of the three provinces, extending towards the southwest. Notably, urban pollution was particularly significant in the urban agglomerations centered around Shanghai, Hangzhou, Nanjing, and Hefei. There were 27 cities that exceeded the national standard daily limit. Changzhou was the city with the most serious NO2 pollution, with the NO2 concentration exceeding the standard for 14 d, followed by Shanghai, with 13 d. In terms of seasonal variation, the order of severity was as follows： winter, autumn, spring, and summer, with the least NO2 pollution occurring on July 9th during the summer, and the most severe NO2 pollution was observed on December 23rd during the winter.

Prototype Alarm Integrating Pulse-Driven Nitrogen Dioxide Sensor Based on Holey Graphene Oxide/In2O3.

NO2 seriously threatens human health and the ecological environment. However, the fabrication of highly sensitive NO2 sensors with rapid response/recovery rates, low detection limits, and ease of integration remains a challenge. Herein, benefiting from the fast carrier transfer and rich active sites, holey graphene oxide (HGO) was adopted to functionalize the In2O3 nanosheet to construct NO2 gas sensors. Characterization and theoretical calculations established the merits of HGO decoration in the NO2 sensing. The optimal sample, 0.5 wt % HGO/In2O3-sheet, exhibited superior sensing properties, resulting in a 1.37-fold improvement in response to 1 ppm of NO2 compared to the GO/In2O3 counterpart. Gas-sensing kinetics analysis revealed its lower activation energy and higher kinetic rate constants. Importantly, pulsed-temperature modulation was employed to decouple the gas adsorption from surface activation processes, achieving an ultrahigh response of 2776 to 1 ppm of NO2 for the 0.5 wt % HGO/In2O3-sheet sensor. Compared to the isothermal mode, this strategy enhanced the response value by 1.6 times, reduced the response/recovery time by 33%/70%, and enabled the detection of NO2 concentrations as low as 1 ppb. Finally, an NO2 monitoring alarm system based on the 0.5 wt % HGO/In2O3-sheet sensor with pulsed-temperature modulation was demonstrated for hazard warnings.

Carbon dots-based dual-mode sensor for highly selective detection of nitrite in food substrates through diazo coupling reaction

As an antioxidant and preservative agent, nitrite (NO2−) plays an essential role in the food industry to maintain freshness or inhibit microbial growth. However, excessive addition of NO2− is detrimental to health, so accurate and portable detection of NO2− is critical for food quality control. Notably, the selectivity of most carbon dots (CDs)-based fluorescence sensors was not enough due to the nonspecific interaction mechanism of hydrogen bond, electrostatic interaction and inner filter effect etc. Herein, a novel fluorescence/UV–vis absorption (FL/UV–vis) dual-mode sensor was developed on basis of mC-CDs, which were prepared by simple solvothermal treatment of m-Phenylenediamine (m-PDA) and cyanidin cation (CC). The fluorescence of these mC-CDs could be selectively responded by NO2− through the specific diazo coupling reaction between NO2− and amino groups on the surface of mC-CDs, thus effectively improving the selectivity of NO2− detection. The CDs-based fluorescence sensor possessed a low detection limit of 0.091 μM and 0.143 μM for FL and UV–vis methods and the excellent linear range of 0.0–60.0 μM. Furthermore, the mC-CDs sensor was employed to detect NO2− in real samples with a recovery rate of 97.11 %–104.15 % for quantitative addition. Moreover, the smartphone-assisted fluorescence sensing platform developed could identify the subtle color changes that could not be distinguished by the naked eye, and had the advantages of fast detection speed and intelligence. More importantly, the portable solid phase sensor based on mC-CDs had been successfully applied to the specific fluorescence identification and concentration monitoring of NO2−. Accordingly, the designed sensor provided a new strategy for the highly selective and convenient sensing of NO2− in food substrates, and paved the way for the wide application of CDs-based nanomaterials in the detection of food safety.

Construction of highly efficient In2O3/SnO2 sensor for real-time NO2 monitoring at near room temperature

NO2 is a dangerous gaseous pollutant, which is also a precursor to the formation of photochemical smog and ozone. It has negative impacts on the environment and human beings at local, regional and even the whole global scale. In this context, it is imperative to develop the efficient sensing materials for detecting NO2 gas. In this study, a near room temperature NO2 sensing material (In2O3/SnO2 heterostructure) with “4S” index (selectivity, sensitivity, speed, stability) was constructed, it overcomes the drawbacks of traditional detection techniques and solves the unavoidable problems of metal oxide semiconductors in the sensing process (high temperature, cross response, weak response at low concentration, etc.). Meanwhile, the synergy between the surface structure and heterostructure interface of In2O3/SnO2 has been thoroughly analyzed. More importantly, this study also sampled and monitored ambient air samples from different geographical locations and different time points in cities, which provides strong support for promoting the application of MOS sensors in real environment.

Optimizing NO2 gas sensor performance: Investigating the influence of cobalt doping on WO3 recovery kinetics for enhanced gas sensing application

Gas sensors utilizing a chemiresistive metal oxide semiconductor (MOS) are extensively employed, particularly for detecting nitrogen dioxide (NO2) at moderate temperatures. However, the gas response and the recovery time are hindering the performance of the MOS. In this research, to enhance the gas response and the recovery time a systematic investigation was performed to explore the impact of cobalt (Co) doping in tungsten trioxide (WO3) and its gas sensing properties. Fascinatingly, the 3 mM% Co-doped WO3 sensor exhibits a remarkable gas sensing response of 20776 % at 10 ppm NO2, showcasing a seven-fold enhancement compared to the pristine WO3 sample at 200 ℃. The findings demonstrated that the sensor exhibited an outstanding repeatability, selectivity and long-term stability of 95 % over 8 weeks. Moreover, the sensor possessed a fast response and recovery time 15 s/23 s as compared to the pristine sensor 26 s/154 s. The exceptional gas-sensing capabilities can be ascribed to the existence of defects (oxygen vacancy) within the structure. These defects effectively boost the surface reactivity of WO3 nanoplates, thereby augmenting their sensitivity and enabling a broad-range sensing performance. As a result, this research demonstrates considerable promise for utilizing environmental NO2 monitoring applications.

An acid-free sensing strategy for detecting nitrite using dihydroquinoline-8-carboxylate as a probe

Nitrite (NO2−) has been identified as a typical pollutant harmful to the human body and heavily assayed in the fields of food safety and water quality control. The mainstream sensing strategies for detecting NO2− depend on Griess reaction or its improved methods which employ Griess reaction to initiate further inter-or intramolecular interaction to generate readout signals. However, a significant drawback of these methods is the use of strongly acidic media. In this study, we designed and synthesized a new NO2−-specific fluorescent probe (ethyl 3-cyano-2-hydroxy-5-imino-8-(3-methoxy-3-oxopropyl)-4-(pyridin-2-yl)-5,8-dihydroquinoline-8-carboxylate, DHQC). DHQC exhibited strong green fluorescence in an acetonitrile-PBS (10 mM) mixed system (pH 7.0). In the neutral medium and at room temperature, the fluorescence of DHQC changed from green to blue with the addition of NO2−. The preliminary mechanistic investigation reveals that NO2− can induce the decarboxylation of the probe DHQC. Based on this finding, a high sensitive and selective method for NO2−-detection was established, which showed good linearity in a range of 5∼50 μM with a limit detection of 3.5 nM (3σ). Given the unique properties of DHQC, a DHQC-loaded hydrogel bead device was further developed and employed for rapid monitoring of NO2−, exhibiting the advantages of simple preparation, high sensitivity, and fast response compared with traditional sensing reagents. In addition, DHQC was also used as a fluorescent probe for cell-imaging in live cells, exhibiting good cell permeability and biocompatibility. This study proposes a potential strategy for constructing smart fluorimetric probes used for NO2− detection.

Cost-Efficient measurement platform and machine-learning-based sensor calibration for precise NO2 pollution monitoring

Air quality significantly impacts human health, the environment, and the economy. Precise real-time monitoring of air pollution is crucial for managing associated risks and developing appropriate short- and long-term measures. Nitrogen dioxide (NO2) stands as a common pollutant, with elevated levels posing risks to the human respiratory tract, exacerbating respiratory infections and asthma, and potentially leading to chronic lung diseases. Notwithstanding, precise NO2 detection typically demands complex and costly equipment. This paper explores NO2 monitoring using low-cost platforms, meticulously calibrated for reliability. An integrated measurement unit is first presented that contains primary and supplementary nitrogen dioxide sensors, as well as auxiliary detectors for evaluating outside and inside temperature and humidity. The calibration process utilizes data acquired over the period of five months from various reference stations. Employing machine learning with an artificial neural network (ANN)-based and kriging interpolation surrogate models, the correction strategy integrates additive and multiplicative enhancement, predicted by the ANN through auxiliary sensor data such as temperature, humidity, and the sensor-detected NO2 levels. Extensive verification studies showcase that this calibration approach notably enhances monitoring precision (coefficient of determination surpassing 0.85 concerning reference data, and RMSE of less than four μg/m3), rendering low-cost NO2 detection practical and dependable.

Validation of cheap axial passive sampler and procedure suitable for atmospheric NO2 community-based monitoring

Nitrogen dioxide (NO2) is an air pollutant highly impacting on human health, and its measurement is crucial for air quality assessment. Use of passive samplers for long-term large-scale monitoring is a reasonably reliable and economic alternative to more sophisticated and expensive equipment employed in active air sampling by environmental control authorities. In recent years the Citizen Science approach, based on low-cost devices, is spreading more and more in environmental control. Passive samplers available on the market (like the consolidated “Palmes” tubes) are often used in community-based monitoring campaigns. We describe validation of a new cheap axial diffusion tube for NO2 monitoring, used in combination with a new user-friendly App for smartphone that represents an innovation to speed up recording of geo-localization and exposition period data. Affordability and availability of materials, simple construction protocol and easy App procedure, allow possible self-production by school students and non-expert users, making the proposed tube a potential tool to realize extended Citizen Science monitoring campaigns. Accuracy within 25% and precision within 20%, evidenced in validation, show comparability of the tube performance with Palmes-type tubes and agreement with the official monitoring station results. Two small-scale trial monitoring campaigns, involving high school students, were performed to test the efficacy of the proposed “tube-App” system in combining educational impact and community value of air quality monitoring.

Optimizing NO2 monitoring network using a background map for spatial heterogeneity stratification

Nitrogen dioxide (NO2) is a critical air pollutant affecting health and the environment. However, existing monitoring station networks often fail to adequately capture the regional distribution of NO2, indicating a need for enhanced sampling strategies. This study focuses on Southwest Fujian in China and introduces a high-precision NO2 background map to delineate spatial stratified heterogeneity. Subsequently, the Mean of Surface with Non-homogeneity (MSN) method was employed to optimize the design of the NO2 monitoring network, proposing the addition of 125 new stations. The Bayesian Kriging analysis, utilized to evaluate the optimized network, resulted in a coefficient of determination (R2) of 0.87, a mean absolute percentage error (MAPE) of 9.61%, a mean absolute error (MAE) of 2.75 μg/m3 and a root mean square error (RMSE) of 2.47 μg/m3. The improved accuracy and efficiency of the NO2 monitoring network were validated against the background map. This research underscores the effectiveness of integrating precise mapping techniques with strategic network optimization for superior environmental monitoring outcomes.

Evaluating Phoenix Metropolitan Area Ozone Behavior Using Ground-Based Sampling, Modeling, and Satellite Retrievals

An oxidizing and harmful pollutant gas, tropospheric ozone is a product of a complex set of photochemical reactions that can make it difficult to enact effective control measures. A better understanding of its precursors including volatile organic compounds (VOCs) and nitrogen oxides (NOx) and their spatial distribution can enable policymakers to focus their control efforts. In this study we used low-cost sensors (LCSs) to increase the spatial resolution of an existing NO2 monitoring network in addition to VOC sampling to better understand summer ozone formation in Maricopa County, Arizona, and observed that afternoon O3 values at the downwind sites were significantly correlated, ~0.27, to the morning NO2 × rate values at the urban sites. Additionally, we looked at the impact of wildfire smoke on ozone exceedances and compared non-smoke days to smoke days. The average O3 on smoke days was approximately 20% higher than on non-smoke days, however, the average NO2 concentration multiplied by estimated photolysis rate (NO2 × rate) values were only 2% higher on smoke days. Finally, we evaluated the ozone sensitivity of the region by calculating HCHO/NO2 ratios using three different datasets: ground, satellite, and model. Although the satellite dataset produced higher HCHO/NO2 ratios than the other datasets, when the proper regime thresholds are applied the three datasets consistently show transition and VOC-limited O3 production regimes over the Phoenix metro area. This suggests a need to implement more VOC emission controls in order to reach O3 attainment in the county.

Gas Detecting Evaluation by Graphitic-based Silicon Carbide Nanosurface doped with Manganese: A Promising Device for Air Pollution Monitoring

Silicon carbide (SiC) as a direct broad bandgap semiconducting material has the potential to bring a considerable developement into optoelectronic and electronic devices. This article aims to investigate Physico-chemical properties of manganese (Mn)–doped graphene-like silicon carbide (SiC) monolayer sheet by the first-principles methods based on the density functional theory (DFT) for scavenging of CO, CO2, NO, NO2 gas molecules. The results recommend that the adsorption of these gas molecules on Mn-embedded SiC sheet monolayer is more energetically desired than that on the pristine ones. Gas molecules of CO, CO2, NO, NO2 have been adsorbed on the Mn site of doped SiC monolayer through the formation of covalent bonds. The assumption of chemical adsorptions has been approved by the projected density of states (PDOS) and charge density difference plots. Charge density difference calculations also indicate that the electronic densities were mainly accumulated on the adsorbate of CO, CO2, NO, NO2 gas molecules. The ability of SiC nanosheet for monitoring of CO, CO2, NO and NO2 is fluctuated by their selectivity and sensitivity through NMR, NQR, IR and HOMO/LUMO approaches which can represent the efficiency of Mn–doped SiC surface as the promising sensors toward air pollution detecting.

Unveiling the urban resilience in cities of China, a study on NO2 concentrations and COVID-19 pandemic

COVID-19 can be considered as the largest public health incident since 2019, posing disturbances to the medical, economic, and social systems. Understanding different levels of city resilience to the impact of COVID-19 on sustainable urban development is therefore essential. In this paper, we analyzed the evolution of the pandemic, the recovery of urban activities and major drivers of urban resilience to COVID-19 based on urban activity patterns derived from NO2 monitoring stations from 217 cities in China. It is observed that nearly all urban activities have been affected by the epidemic with a reduced NO2 emission, indicating a significant decline in the intensity of urban activity. The recovery patterns of human activity among different cities show that: (1) northern cities where low-resilience cities agglomerated have been severely affected. As of April 31, 2020, 12 northern cities have not recovered to pre-epidemic levels; (2) about three-quarters of the cities went through multiple stages to return to the pre-pandemic level, which related to both pandemic stress and prevention measures; (3) about 46.04% of the cities experienced increasing activity patterns that exceeded the pre-epidemic activity level. It is also observed that urban resilience can behave quite differently driven by variations in green coverage, economic aggregate, employment security, and medical system. Our study highlights the potential of an elevated urban resilience by optimizing the urban layout, improving the quantity and quality of green space, and enhancing the medical system.