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The development and renewal of gas sensor technology have enabled more and more low-cost gas sensors to form a carbon monitoring network to meet the requirements of the city. In the context of China's commitment to achieving the "double carbon" target by 2060, this paper reviews the principles of four standard gas sensors and the application of several low-cost sensors in urban carbon monitoring networks, with the aim of providing a practical reference for the future deployment of carbon monitoring networks in Chinese cities. Moreover, the types, prices, and deployment of the sensors used in each project are summarized. Based on this review, non-dispersive infrared sensors have the best performance among the sensors and are commonly used in many cities. Lots of urban climate networks in cities were summarized by many reviews in the literature, but only a few sensors were studied, and they did not consider carbon dioxide (CO2) sensors. This review focuses on the dense CO2 urban monitoring network, and some case studies are also discussed, such as Seoul and San Francisco. To address the issue of how to better ensure the balance between cost and accuracy in the deployment of sensor networks, this paper proposes a method of simultaneously deploying medium-precision and high-precision fixed sensors and mobile sensors to form an urban carbon monitoring network. Finally, the prospects and recommendations, such as different ways to mitigate CO2 and develop an entire carbon monitoring system for future urban carbon monitoring in China, are also presented. [ABSTRACT FROM AUTHOR]

Methane (CH4) is the second most potent greenhouse gas (GHG), and China emerges as the largest anthropogenic CH4 emitter by country. Current limited CH4 monitoring systems in China are unfortunately inadequate to support carbon management. Here we use the Weather Research and Forecasting model (WRF) coupled with a GHG module and satellite constrained emissions to simulate the spatiotemporal distribution of CH4 over East Asia in 2017. Model evaluations using both satellite retrievals and ground-based observations indicate reliable performance. We further inter-compare four proper orthogonal decomposition (POD)-based sensor placement algorithms and find they are able to capture main spatial features of surface CH4 under an oversampled condition. The QR pivot algorithm exhibits superiority in capturing high CH4, and it offers the best reconstruction with both high efficiency and accuracy. Areas with high CH4 concentrations and intense anthropogenic activities remain underrepresented by current CH4 sampling studies, leading to notable reconstruction error over central and eastern China. Optimal planning of 160 sensors guided by the QR pivot algorithm can yield reasonable reconstruction performance and costs of site construction. Our results can provide valuable references for future planning of CH4 monitoring sites. [ABSTRACT FROM AUTHOR]

An early contamination warning system with deployed water quality sensors is often used to enhance the safety of a water distribution system (WDS). While algorithms have been developed to select an optimal water quality sensor placement strategy (WQSPS) for WDSs, many of them do not account for the influences caused by future uncertainties, such as sensor failures and system changes (e.g., demand variations and configuration/expansion changes in the WDS). To this end, this paper proposes a comprehensive framework to evaluate the robustness of WQSPSs to these possible uncertainties. This is achieved by considering five different performance objectives of WQSPSs as well as possible future demand and typology variations of WDSs under a wide range of sensor failure scenarios. More specifically, an optimization problem is formulated to evaluate the robustness of the WQSPSs, in which an evolutionary-based optimization approach coupled with an efficient data-archive method is used to solve this optimization problem. The framework is demonstrated on two real-world WDSs in China. The results show that: (1) the WQSPS's robustness can be highly dependent on the performance objectives considered, implying that an appropriate objective needs to be carefully selected for each case driven by practical needs, (2) the WDS's demand and configuration changes can have a significant influence on the WQSPS's robustness, in which the solution with more sensors in or close to the affected area is likely to better cope with these system changes, and (3) the proposed framework enables critical sensors to be identified, which can then be targeted for prioritizing maintenance actions. [ABSTRACT FROM AUTHOR]

Offshore wind turbines (OWTs) experience persistent nonlinear vibrations due to constantly changing environmental and operational conditions. These nonlinear dynamic responses significantly affect structural safety and introduce uncertainties in predicting fatigue and service life. However, understanding the complex nonlinear characteristics of OWTs is challenging because multiple factors collectively contribute to structural nonlinear vibrations. To quantitatively assess the nonlinear effects on OWT dynamic responses during operation, this paper systematically analyzes long-term monitoring data from a 4 MW monopile OWT site at Rudong Wind Farm in China using a nonlinear effect separation method based on a time-varying kernel function. This method separates the nonlinear components using the measured wave surface elevation and dynamic response data and statistically analyzes the long-term variation of nonlinear effect by combining comprehensive evaluation indicators. The correctness of the method applied to OWTs has been first verified using the shutdown condition data under calm sea conditions. Then, the influence of operating conditions, sensor placement, rotor speed, and other parameters on the nonlinear effects have been investigated through statistical analysis of the real-time evaluation results to reveal the nonlinear evolution process and its contributing factors. An unexpected discovery emerged during the monitoring process: the dynamic response of the OWT during the passage of Typhoon 'In-FA' was measured. Consequently, this paper emphasizes the abnormal nonlinear behavior of OWTs under typhoon conditions. The analysis results indicate that under typhoon conditions, the nonlinear effects in the structural response substantially increase, potentially contributing over 20% to the overall response. This finding suggests that current structural designs do not fully consider the nonlinear effects, potentially posing risks to safe operation. The insights from this study will also contribute to the structural design of OWTs, aiding in risk reduction and economic loss mitigation. • A novel systematic study on the nonlinear performances of OWT is conducted. • A nonlinear model for nonlinear component separation is established. • Real-time evaluation and statistical characteristics analysis are realized. • Influence factors of nonlinear effects of OWTs in field conditions are discussed. • Influences of the typhoon on nonlinear performances are discussed. [ABSTRACT FROM AUTHOR]

The excellent performance of geometric positioning of a newly launched satellite will greatly broaden its application field. Launched on January 15, 2020, the Hongqi-1-H9 wide-range satellite is the largest sub-meter-level satellite worldwide and the first ton-level commercial remote sensing satellite in China, with a resolution of less than 1 m and a swath width of 136 km. This study was aimed at assessing the geometric positioning accuracy of this newly launched satellite considering three aspects, namely, the circle error accuracy, rational polynomial coefficient-based direct geometric positioning accuracy and ground control point-based absolute positioning accuracy under urban, plain, and mountainous areas, with different topographies. The results of the conducted experimental investigation indicated that the Hongqi-1-H9 satellite could exhibit a high positioning accuracy in planar and vertical directions for different terrains. In particular, for our experimental areas with a low topography and few surface structures, the geometric positioning accuracy of the Hongqi-1-H9 satellite imagery was less than 4m and 2 m in the planimetry and elevation directions, respectively. These characteristics can promote the application of the Hongqi-1-H9 satellite images in agricultural surveys, target detection, and land surveys, among other domains. [ABSTRACT FROM AUTHOR]

Summary: This paper introduces a new and unique full‐scale testbed site for structural health monitoring and soil–structure system identification studies being developed in southwest China. The site is a 48‐story skyscraper with an extended four‐level basement, supported by piles, located in Kunming, the capital of Yunnan Province. Located in the diffused zone of collision of the Indian and Eurasian tectonic plates, Yunnan is one of the most active seismic areas in mainland China. The final sensor deployment will consist of 43 triaxial accelerometers (129 channels) and one weather station. The accelerometer array comprises (a) a structural array of 25 accelerometers installed at 10 levels aboveground, (b) a basement array of 14 accelerometers distributed in the first and fourth basements, and (c) two borehole arrays installed close to the basement perimeter wall, each with one accelerometers at the surface and another one at 50‐m depth, which is the depth reached by the piles. With such dense instrumentation of structure, basement, and pile foundation, this site will be the first permanently instrumented full‐scale testbed to enable identification of a soil‐foundation–basement‐structure system and validation of many assumptions commonly made in the prediction of the soil–structure interaction effects. A high‐performance wired local area network has been installed in the building, featuring a Precision Time Protocol‐enabled time synchronization and real‐time remote access over the Internet. The site will be fully operational in late spring of 2020. Results of preliminary system identification of the structure from ambient vibration test data are presented. [ABSTRACT FROM AUTHOR]

In order to improve the transverse alignment accuracy of the control network in Chinese Spallation Neutron Source (CSNS) Linac, a 200-m stretched wire-based system was established in the tunnel. A wire position sensor based on bi-telecentric technology (tWPS) was specially designed to obtain the offset with respect to the stretched wire. A control network adjustment algorithm with additional wire constraints has been developed. The laser tracker measured station by station in a free station way, and measured the control network and the fiducials of the tWPS at the same time. The offset observation equation was combined with the angle observation equation and distance observation equation of the laser tracker for joint solution. As a result, the transverse accuracy of the control network was improved from 0.061 mm without wire constraint to 0.028 mm with wire constraint, which indicates that the addition of wire constraints can indeed effectively improve the transverse accuracy of the longitudinal control network. [ABSTRACT FROM AUTHOR]

[Display omitted] • State of the art on real-time sensors for indoor air evaluation is reviewed. • High spatiotemporal resolution data from real-time sensors is favorable for the risk assessment of indoor air pollution. • Challenges including performance, stability and calibration of sensors still remained. • Sensors with high performance, low price, and more target pollutants are expected. People generally spend most of their time indoors, making indoor air quality be of great significance to human health. Large spatiotemporal heterogeneity of indoor air pollution can be hardly captured by conventional filter-based monitoring but real-time monitoring. Real-time monitoring is conducive to change air assessment mode from static and sparse analysis to dynamic and massive analysis, and has made remarkable strides in indoor air evaluation. In this review, the state of art, strengths, challenges, and further development of real-time sensors used in indoor air evaluation are focused on. Researches using real-time sensors for indoor air evaluation have increased rapidly since 2018, and are mainly conducted in China and the USA, with the most frequently investigated air pollutants of PM 2.5. In addition to high spatiotemporal resolution, real-time sensors for indoor air evaluation have prominent advantages in 3-dimensional monitoring, pollution peak and source identification, and short-term health effect evaluation. Huge amounts of data from real-time sensors also facilitate the modeling and prediction of indoor air pollution. However, challenges still remain in extensive deployment of real-time sensors indoors, including the selection, performance, stability, as well as calibration of sensors. In future, sensors with high performance, long-term stability, low price, and low energy consumption are welcomed. Furthermore, more target air pollutants are also expected to be detected simultaneously by real-time sensors in indoor air monitoring. [ABSTRACT FROM AUTHOR]

• A soil moisture data-driven method for ET a was tested for the heterogeneous soils. • The soil hydraulic parameters were optimized using a genetic algorithm procedure. • ET a at sites of different soil textures and crop species were accurately estimated. • The inaccurate soil parameters and stratification may bias the estimates of ET a. • The lowest sensor should be placed away from the interface of stratified soils. A soil moisture data-driven method through inversely solving the Richards' equation was tested, and proved to be a reliable alternative estimator of actual evapotranspiration (ET a) for relatively homogeneous soils in an arid region. Few studies have examined the feasibility of the method using soil moisture for soils with more heterogeneities, which may lead to biased parameters and varied root depths, and thus inaccurate estimates of ET a. We used a genetic algorithm to optimize the parameters most sensitive to ET a estimation, then estimated daily and seasonal ET a using hourly soil moisture data, measured from four sites (from surface to 2-m depth at 20-cm intervals) with different soil textures and crop species in desert-oasis farmlands in northwest China. The seasonal ET a values were 487, 574, 436, and 377 mm at the sites cropped with field maize, spring wheat, seed maize, and seed maize, respectively, in 2018. The daily rates from one site were validated against the eddy-covariance measurements, and the daily rates from all the sites were compared against the estimations from a simple water balance method, and both showed good consistency (R2 = 0.72-0.93). The inverse method has great potential for relatively accurate estimates of ET a in the middle and late growing stages but may induce a slight underestimation in the initial growing season. To determine the minimum monitoring depths of sensors for relatively accurate estimates, ET a values were estimated using soil moisture data at various observation depths, and were compared with eddy-covariance measurements and hypothetical ET a -observed depth curves. The results showed that inaccurate stratification of soil types in the profiles may cause great uncertainty in the estimates, and if texture-contrasting interlayers exist in the profiles, the lowest soil moisture sensor should be placed sufficiently deep, with all the interlayers fully included. [ABSTRACT FROM AUTHOR]

During the past decade, falling has been one of the top three causes of death amongst firefighters in China. Even though there are many studies on fall-detection systems (FDSs), the majority use a single motion sensor. Furthermore, few existing studies have considered the impact sensor placement and positioning have on fall-detection performance; most are targeted toward fall detection of the elderly. Unfortunately, floor cracks and unstable building structures in the fireground increase the difficulty of detecting the fall of a firefighter. In particular, the movement activities of firefighters are more varied; hence, distinguishing fall-like activities from actual falls is a significant challenge. This study proposed a smart wearable FDS for firefighter fall detection by integrating motion sensors into the firefighter's personal protective clothing on the chest, elbows, wrists, thighs, and ankles. The firefighter's fall activities are detected by the proposed multisensory recurrent neural network, and the performances of different combinations of inertial measurement units (IMUs) on different body parts were also investigated. The results indicated that the sensor fusion of IMUs from all five proposed body parts achieved performances of 94.10%, 92.25%, and 94.59% in accuracy, sensitivity, and specificity, respectively. [ABSTRACT FROM AUTHOR]

Low-cost sensors (LCSs) are widely acknowledged for bringing a paradigm shift in supplemental traditional air monitoring by air regulatory agencies. However, there is concern regarding its data quality and performance stability, which has greatly restricted its large-scale applications. Knowing the recent techniques, progress, and challenges of LCS calibration is of immense significance to promote the field of environmental monitoring. By summarizing the published evidence, this review shows that the global sensor market is rapidly expanding due to the surging needs, but the calibration efforts have been focused on a limited selection of sensors. Relative humidity correction, regression, and machine learning are the three mainstream calibration techniques. Although there is no one-size-fits-all solution, a feature of the latest research tendency is machine learning. The duration of calibration is largely neglected in the experiment design, but it is found to affect the performance of different calibration methods, especially those that are data-driven. Geographically, China and the United States gained the most research attention in the sensor calibration field, but the spatial mismatch between particulate matter hotspots and calibration sites is quite evident for the rest of the world. Incomplete and unevenly distributed research footprints could limit the large-scale test of method generalizability, as well as diminish the monitoring capacity in underserved areas that suffer greater environmental justice crises. In general, model performance is enhanced by including the key influencing factors, but the degree of improvement is not evidently related to the number of explanatory variables. Overall, studies prove the critical importance of field calibration before sensor deployment, but more studies are needed to establish experiment protocols that can be customized to specific needs. • Calibration methods and duration collectively determine the calibration performance. • Machine learning methods have gained growing interest and popularity in sensor calibration. • Globally, spatial mismatch between PM 2.5 hotspots and calibration sites is evident. • Adding more influencing variables improves calibration accuracy but not evidently. [ABSTRACT FROM AUTHOR]

Low-cost sensors (LCS) for air-quality monitoring have shown huge potential in enhancing spatial and temporal resolutions at a lower cost, greater flexibility in use and with less maintenance than fixed-air quality monitoring stations (AQMS). There are numerous reports showing sensor-based systems perform well under laboratory conditions. However, the accuracy of LCS operated under field conditions has been reported to deviate from their use in these controlled environments. Previous studies have employed various mathematical techniques to improve system performance. These include classic multivariate regression model (MLR) to incorporate ambient factors (e.g. temperature & relative humidity) and machine learning (ML) models for integrating complex non-linear sensor responses for gaseous pollutant concentration predictions. However, routine field calibrations may cause data inconsistency during measurement periods. This study first illustrates the limitations of short-term routine calibration by comparing performances of existing MLR and ML models. Further, based on a long-term evaluation period, a newly principle-based method named Temperature Look-Up (TLU) model was built and compared with the existing MLR and ML models. Measurements were taken for 8 months (August 2017 to March 2018) in SanMenXia (SMX) city, China, using 8 sets of sensor systems (Mini Air Stations, MAS). One MAS was co-located with AQMS to provide data to assess the principle, whereas the other 7 MAS were used for verification. The TLU model showed the best performance for long term validation (4 months) and high model inner coherence (R 2 > 0.91). Meanwhile, the TLU model also shows the fast convergence (40 days) to reach stable prediction performance, and spatial representativeness in the sensor network. Recommendations on sensor network deployment and strategy for data maintenance based on our observations are discussed. Image 1 • A principle-based calibration model was introduced for electrochemical NO 2 sensor long-term application. • Comparison of machine learning algorithms was conducted. • The new model was verified for its spatiotemporal robustness in the network. [ABSTRACT FROM AUTHOR]