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Abstract The CMS detector, including its muon system, has been operating at the CERN LHC in increasingly challenging conditions for about 15 years. The muon detector was designed to provide excellent triggering and track reconstruction for muons produced in proton–proton collisons at an instantaneous luminosity ( $$\mathcal {L}$$ L ) of $$1 \times 10^{34}$$ 1 × 10 34 cm $$^{-2}$$ - 2 s $$^{-1}$$ - 1 . During the Run 2 data-taking period (2015–2018), the LHC achieved an instantaneous luminosity of twice its design value, resulting in larger background rates and making the efficient detection of muons more difficult. While some backgrounds result from natural radioactivity, cosmic rays, and interactions of the circulating protons with residual gas in the beam pipe, the dominant source of background hits in the muon system arises from proton–proton interactions themselves. Charged hadrons leaving the calorimeters produce energy deposits in the muon chambers. In addition, high-energy particles interacting in the hadron calorimeter and forward shielding elements generate thermal neutrons, which leak out of the calorimeter and shielding structures, filling the CMS cavern. We describe the method used to measure the background rates in the various muon subsystems. These rates, in conjunction with simulations, can be used to estimate the expected backgrounds in the High-Luminosity LHC. This machine will run for at least 10 years starting in 2029 reaching an instantaneous luminosity of $$\mathcal {L} = 5 \times \text {10}^\text {34}\,\text {cm}^\text {-2}\,\text {s}^\text {-1}$$ L = 5 × 10 34 cm -2 s -1 and increasing ultimately to $$\mathcal {L} = 7.5 \times \text {10}^\text {34}\,\text {cm}^\text {-2}\,\text {s}^\text {-1}$$ L = 7.5 × 10 34 cm -2 s -1 . These background estimates have been a key ingredient for the planning and design of the muon detector upgrade.

Accurate estimation of the wind profile, especially in the lowest few hundred meters of the atmosphere, is of great significance for the weather, climate, and renewable energy sector. Nevertheless, the Monin–Obukhov similarity theory fails above the surface layer over a heterogeneous underlying surface, causing an unreliable wind profile to be obtained from conventional extrapolation methods. To solve this problem, we propose a novel method called the PLM-RF method that combines the power-law method (PLM) with the random forest (RF) algorithm to extend wind profiles beyond the surface layer. The underlying principle is to treat the wind profile as a power-law distribution in the vertical direction, with the power-law exponent (α) determined by the PLM-RF model. First, the PLM-RF model is constructed based on the atmospheric sounding data from 119 radiosonde (RS) stations across China and in conjunction with other data such as surface wind speed, land cover type, surface roughness, friction velocity, geographical location, and meteorological parameters from June 2020 to May 2021. Afterwards, the performance of the PLM-RF, PLM, and RF methods over China is evaluated by comparing them with RS observations. Overall, the wind speed at 100 m from the PLM-RF model exhibits high consistency with RS measurements, with a determination coefficient (R2) of 0.87 and a root mean squared error (RMSE) of 0.92 m s−1. By contrast, the R2 and RMSE of wind speed results from the PLM (RF) method are 0.75 (0.83) and 1.37 (1.04) m s−1, respectively. This indicates that the estimates from the PLM-RF method are much closer to observations than those from the PLM and RF methods. Moreover, the RMSE of the wind profiles estimated by the PLM-RF model is relatively large for highlands, while it is small for plains. This result indicates that the performance of the PLM-RF model is affected by the terrain factor. Finally, the PLM-RF model is applied to three atmospheric radiation measurement sites for independent validation, and the wind profiles estimated by the PLM-RF model are found to be consistent with Doppler wind lidar observations. This confirms that the PLM-RF model has good applicability. These findings have great implications for the weather, climate, and renewable energy sector.

Salt intrusion in estuaries has been exacerbated by climate change and human activities. Previous studies have primarily focused on salt intrusion in the mainstem of estuaries, whereas those in sub-estuaries (those that branch off their main estuaries) have received less attention. During an extended La Niña event from 2021 to 2022, a sub-estuary (the East River estuary) alongside the Pearl River estuary, China, experienced severe salt intrusions, posing a threat to the freshwater supply in the surrounding area. Observations revealed that maximum salinities in the main estuary typically preceded spring tides, exhibiting significant asymmetry in salinity rise and fall over a fortnightly timescale. In contrast, in the upstream region of the sub-estuary, the variation in salinity was in phase with that of the tidal range, and the rise and fall of the salinity were more symmetrical. Inspired by these observations, we employed idealized numerical models and analytical solutions to investigate the underlying physics behind these behaviors. It was discovered that under normal dry conditions (with a river discharge of 1500 m3 s−1 at the head of the main estuary), the river–tide interaction and change in horizontal dispersion accounted for the in-phase relationship between the salinity and tidal range in the upstream region of the sub-estuary. Under extremely dry conditions (i.e., a river discharge of 500 m3 s−1 at the head of the main estuary), salinity variations were in phase with those of the tidal range in the middle as well as the upstream region of the sub-estuary. The variation in salinity in the main estuary along with those in salt dispersion and freshwater influx inside the sub-estuary collectively influenced salinity variation in the well-mixed sub-estuary. These findings have important implications for water resource management and salt intrusion prevention in the catchment area.

Below-cloud scavenging (BCS) is the process of aerosol removal from the atmosphere between cloud base and the ground by precipitation (e.g., rain or snow), and affects aerosol number or mass concentrations, size distribution, and lifetime. An accurate representation of precipitation phases is important in treating BCS as the efficiency of aerosol scavenging differs significantly between liquid and solid precipitation. The impact of different representations of BCS on existing model biases was examined through implementing a new aerosol BCS scheme in the Environment and Climate Change Canada (ECCC) air quality prediction model GEM-MACH and comparing it with the existing scavenging scheme in the model. Further, the current GEM-MACH employs a single-phase precipitation for BCS: total precipitation is treated as either liquid or solid depending on a fixed environment temperature threshold. Here, we consider co-existing liquid and solid precipitation phases as they are predicted by the GEM microphysics. GEM-MACH simulations, in a local-area domain over the Athabasca oil sands areas, Canada, are compared with observed precipitation samples, with a focus on the particulate base cation NH4+; acidic anions NO3-, SO4=, HSO3- in precipitation; and observed ambient particulate sulfate, ammonium, and nitrate concentrations. Overall, the introduction of the multi-phase approach and the new scavenging scheme enhances GEM-MACH performance compared to previous methods. Including a multi-phase approach leads to altered SO4= scavenging and impacts the BCS of SO2 into the aqueous phase over the domain. Sulfate biases improved from +46 % to −5 % relative to Alberta Precipitation Quality Monitoring Program wet sulfate observations. At Canadian Air and Precipitation Monitoring Network stations the biases became more negative, from −10 % to −30 % for the tests carried out here. These improvements contrast with prior annual average biases of +200 % for SO4=, indicating enhanced model performance. Improvements in model performance (via scores for correlation coefficient, normalized mean bias, and/or fractional number of model values within a factor of 2 of observations) could also be seen between the base case and the two simulations based on multi-phase partitioning for NO3-, NH4+, and SO4=. Whether or not these improvements corresponded to increases or decreases in NO3- and NH4+ wet deposition varied over the simulation region. The changes were episodic in nature – the most significant changes in wet deposition were likely at specific geographic locations and represent specific cloud precipitation events. The changes in wet scavenging resulted in a higher formation rate and larger concentrations of atmospheric particle sulfate.

This study evaluates tropospheric columns of methane, carbon monoxide, and ozone in the Arctic simulated by 11 models. The Arctic is warming at nearly 4 times the global average rate, and with changing emissions in and near the region, it is important to understand Arctic atmospheric composition and how it is changing. Both measurements and modelling of air pollution in the Arctic are difficult, making model validation with local measurements valuable. Evaluations are performed using data from five high-latitude ground-based Fourier transform infrared (FTIR) spectrometers in the Network for the Detection of Atmospheric Composition Change (NDACC). The models were selected as part of the 2021 Arctic Monitoring and Assessment Programme (AMAP) report on short-lived climate forcers. This work augments the model–measurement comparisons presented in that report by including a new data source: column-integrated FTIR measurements, whose spatial and temporal footprint is more representative of the free troposphere than in situ and satellite measurements. Mixing ratios of trace gases are modelled at 3-hourly intervals by CESM, CMAM, DEHM, EMEP MSC-W, GEM-MACH, GEOS-Chem, MATCH, MATCH-SALSA, MRI-ESM2, UKESM1, and WRF-Chem for the years 2008, 2009, 2014, and 2015. The comparisons focus on the troposphere (0–7 km partial columns) at Eureka, Canada; Thule, Greenland; Ny Ålesund, Norway; Kiruna, Sweden; and Harestua, Norway. Overall, the models are biased low in the tropospheric column, on average by −9.7 % for CH4, −21 % for CO, and −18 % for O3. Results for CH4 are relatively consistent across the 4 years, whereas CO has a maximum negative bias in the spring and minimum in the summer and O3 has a maximum difference centered around the summer. The average differences for the models are within the FTIR uncertainties for approximately 15 % of the model–location comparisons.

The current status as well as the potential absorption capability of the forest carbon sinks in terrestrial ecosystems are in urgent need of further studies. The ground observation-based methods are labor-intensive, and the resulting statistics from samples are difficult to evaluate, while inversion methods based on remote sensing data lack theoretical explanation and universality. This paper proposes a pixel-level, multi-scale, high-precision Explicit Forest carbon stock Model (EFM) that is universal and adaptive. First, four key variables were used in the construction of the EFM: remote sensing image resolution, forest canopy density, terrain slope, and forest height; Second, simulated forest scene were generated based on the growth characteristics of individual trees, and EFM parameters were solved and analyzed by these simulated pixels; Third, the EFM was tested at various scales and forest saturation levels to verify its accuracy, robustness, and applicability, the simulation and real-life experiments show that the correlation coefficient is greater than 0.98 and the relative error is about 20%. The EFM solves the problem that the existing methods are lack in theoretical interpretation and universal applicability, thus can be used to map forest carbon stocks at high resolution and large scale and even monitor forest carbon dynamics at global scale.

Changes in aerosol loadings and properties are important for the understanding of atmospheric environment and climate change. This study investigates the characteristics and long-term trends of aerosols of different sizes and types in Asia from 2000 to 2020 by considering multi-source aerosol data and novel analysis methods and perspectives, all of which provides the groundwork for promoting the acquisition of new discoveries that are different from the past. The geometric mean aggregation method is applied, and serial autocorrelation is considered to avoid the overestimation of trend significance. Among regions in Asia, high values of aerosol optical depth (AOD) are mainly concentrated in East Asia (EA) and South Asia (SA) and are closely related to the population density. The AOD in EA showed the most significant negative trend, with a value of -5.28×10-4 per year, mainly owing to decreases in organic carbon (OC), black carbon (BC), and dust aerosols. It is also worth noting that this observed large-scale decrease in OC and BC is a unique and significant phenomenon to region of EA and mainly around China. In contrast, the aerosol concentrations in SA generally show a positive trend, with an increased value of AOD of 1.25 × 10−3 per year. This increase is mainly due to large emissions of fine-mode aerosols, such as OC and sulfate aerosol. Additionally, the high aerosol loading in northern SA has a lower AOD variability when compared with that of East China Plain, revealing a relatively more persistent air pollution situation. Over the whole region of Asia, the characteristics of the percentage changes in different types of AOD are seen in the increases in BC (6.23 %) and OC (17.09 %) AOD, together with a decrease in dust (−5.51 %), sulfate (−3.07 %), and sea salt (−9.80 %) AOD. Except for anthropogenic emissions, the large increase in the percentage of OC is also due to wildfires found in northern Asia in the summer, whereas the differently sized AOD only shows slight changes in Asia in that small-sized AOD decreases (−3.34 %) and that the total AOD did not show a significant change. This suggests that, from a trend perspective, decreases in aerosol in recent years have mostly been offsetting earlier increases in the anthropogenic emissions over Asia. To summarize, the above findings analyse the comprehensive characteristics of aerosol distributions and reappraise the long-term trends of different aerosol parameters, which will greatly enhance the understanding of regional and global aerosol environment and climatology, in addition to filling the gaps and breaking through the limitations of past knowledge.

Accurate estimation of wind speed at wind turbine hub height is of significance for wind energy assessment and exploitation. Nevertheless, the traditional power law method (PLM) generally estimates the hub-height wind speed by assuming a constant exponent between surface and hub-height wind speed. This inevitably leads to significant uncertainties in estimating the wind speed profile especially under unstable conditions. To minimize the uncertainties, we here use a machine learning algorithm known as random forest (RF) to estimate the wind speed at hub heights such as at 120 m (WS120), 160 m (WS160), and 200 m (WS200). These heights go beyond the traditional wind mast limit of 100–120 m. The radar wind profiler and surface synoptic observations at the Qingdao station from May 2018 to August 2020 are used as key inputs to develop the RF model. A deep analysis of the RF model construction has been performed to ensure its applicability. Afterwards, the RF model and the PLM model are used to retrieve WS120, WS160, and WS200. The comparison analyses from both RF and PLM models are performed against radiosonde wind measurements. At 120 m, the RF model shows a relatively higher correlation coefficient R of 0.93 and a smaller RMSE of 1.09 m s−1, compared with the R of 0.89 and RMSE of 1.50 m s−1 for the PLM. Notably, the metrics used to determine the performance of the model decline sharply with height for the PLM model, as opposed to the stable variation for the RF model. This suggests the RF model exhibits advantages over the traditional PLM model. This is because the RF model considers well the factors such as surface friction and heat transfer. The diurnal and seasonal variations in WS120, WS160, and WS200 from RF are then analyzed. The hourly WS120 is large during daytime from 09:00 to 16:00 local solar time (LST) and reach a peak at 14:00 LST. The seasonal WS120 is large in spring and winter and is low in summer and autumn. The diurnal and seasonal variations in WS160 and WS200 are similar to those of WS120. Finally, we investigated the absolute percentage error (APE) of wind power density between the RF and PLM models at different heights. In the vertical direction, the APE is gradually increased as the height increases. Overall, the PLM algorithm has some limitations in estimating wind speed at hub height. The RF model, which combines more observations or auxiliary data, is more suitable for the hub-height wind speed estimation. These findings obtained here have great implications for development and utilization in the wind energy industry in the future.

Global warming has led to increased compound hazards, and an accurate risk assessment of such hazards is of great importance to urban emergency management. Due to the interrelations between multiple hazards, the risk assessment of a compound hazard faces several challenges: (1) the evaluation of hazard level needs to consider the correlations between compound hazard drivers, (2) usually only a small number of data samples are available for estimating the joint probability distribution of the compound hazard drivers and the loss caused by the hazards, and (3) the risk assessment process often ignores the temporal dynamics of compound hazard occurrences. This paper aims to address the mentioned challenges and develop an integrated risk assessment model VFS–IEM–IDM to quantify the dynamic risk of compound hazards based on variable fuzzy set theory (VFS), information entropy method (IEM), and information diffusion method (IDM). For the first challenge, VFS–IEM–IDM measures the effect of the compound hazard drivers via the use of relative membership degree and analyses the correlation between drivers with the entropy weight method, which is combined to evaluate compound hazard level. To address the second challenge, VFS–IEM–IDM applies the normal diffusion function to estimate the probability distribution of the compound hazard and the corresponding loss vulnerability curve. To deal with the third challenge, VFS–IEM–IDM assesses the risk of a compound hazard in different months based on the definition of probabilistic risk. In the end, this paper takes the typhoon–rainstorm disaster in Shenzhen, China, as an example to evaluate the effectiveness of the proposed VFS–IEM–IDM model. The results show that VFS–IEM–IDM effectively estimates the typhoon–rainstorm compound hazard level and assesses the dynamic risk of the compound hazards.

There are plenty of monitoring methods to quantify gas emission rates based on gas concentration measurements around the strong sources. However, there is a lack of quantitative models to evaluate methane emission rates from coal mines with less prior information. In this study, we develop a genetic algorithm–interior point penalty function (GA-IPPF) model to calculate the emission rates of large point sources of CH4 based on concentration samples. This model can provide optimized dispersion parameters and self-calibration, thus lowering the requirements for auxiliary data accuracy. During the Carbon Dioxide and Methane Mission (CoMet) pre-campaign, we retrieve CH4-emission rates from a ventilation shaft in Pniówek coal mine (Silesia coal mining region, Poland) based on the data collected by an unmanned aerial vehicle (UAV)-based AirCore system and a GA-IPPF model. The concerned CH4-emission rates are variable even on a single day, ranging from 621.3 ± 19.8 to 1452.4 ± 60.5 kg h−1 on 18 August 2017 and from 348.4 ± 12.1 to 1478.4 ± 50.3 kg h−1 on 21 August 2017. Results show that CH4 concentration data reconstructed by the retrieved parameters are highly consistent with the measured ones. Meanwhile, we demonstrate the application of GA-IPPF in three gas control release experiments, and the accuracies of retrieved gas emission rates are better than 95.0 %. This study indicates that the GA-IPPF model can quantify the CH4-emission rates from strong point sources with high accuracy.

Aerosol spatial distribution obtained from satellite sensors is critical for understanding regional aerosol environments, anthropogenic aerosol emissions, and global climate change. The Directional Polarimetric Camera (DPC) is the first generation of multi-angle polarized sensor developed by China. It is on-board the GaoFen-5 satellite, running in 705 km sun-synchronous orbit with a 13:30 LT (local time) ascending node. The sensor has three polarized channels at 490, 670, and 865 nm and ∼ 9 viewing angles, mainly used for observing aerosols. The spatial resolution is ∼ 3.3 km at nadir, and global coverage is ∼ 2 d. In this study, the performance of aerosol optical depth (AOD) retrievals from the DPC/GaoFen-5 using the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm were evaluated on a global basis for the first time. The results showed that the DPC GRASP/Model scheme, which used several forms of aerosol-type mixing, achieved good performance. By comparing with Aerosol Robotic Network (AERONET) observations, the correlation coefficient (R), root-mean-square error (RMSE), and expected error (EE%, ± (0.05+0.15×AOD)) were 0.9007 %, 0.0662 %, and 82.54 %, respectively. The scattering angle, number of averaged pixels, length of time steps, and radiative and polarized fitting residuals showed impacts on the results of AOD retrieval in the DPC GRASP/Model scheme. From the most AERONET sites, the R and EE% were larger than ∼ 0.9 % and ∼ 80%. Compared with Moderate resolution Imaging Spectroradiometer (MODIS) products, the spatial and temporal variations of aerosol could be caught by the DPC with the GRASP/Model scheme, showing a good performance. However, values of AOD were also underestimated by DPC, probably due to an overly strict cloud mask. The above findings validated the ability of the DPC sensor to monitor aerosols. It should contribute to the development of aerosol parameter retrieval from multi-angle polarized sensors in the future.

While carbon dioxide is the main cause for global warming, modeling short-lived climate forcers (SLCFs) such as methane, ozone, and particles in the Arctic allows us to simulate near-term climate and health impacts for a sensitive, pristine region that is warming at 3 times the global rate. Atmospheric modeling is critical for understanding the long-range transport of pollutants to the Arctic, as well as the abundance and distribution of SLCFs throughout the Arctic atmosphere. Modeling is also used as a tool to determine SLCF impacts on climate and health in the present and in future emissions scenarios. In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models by assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets. Model simulations over 4 years (2008–2009 and 2014–2015) conducted for the 2022 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship, and aircraft-based observations. The annual means, seasonal cycles, and 3-D distributions of SLCFs were evaluated using several metrics, such as absolute and percent model biases and correlation coefficients. The results show a large range in model performance, with no one particular model or model type performing well for all regions and all SLCF species. The multi-model mean (mmm) was able to represent the general features of SLCFs in the Arctic and had the best overall performance. For the SLCFs with the greatest radiative impact (CH4, O3, BC, and SO42-), the mmm was within ±25 % of the measurements across the Northern Hemisphere. Therefore, we recommend a multi-model ensemble be used for simulating climate and health impacts of SLCFs. Of the SLCFs in our study, model biases were smallest for CH4 and greatest for OA. For most SLCFs, model biases skewed from positive to negative with increasing latitude. Our analysis suggests that vertical mixing, long-range transport, deposition, and wildfires remain highly uncertain processes. These processes need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment. As model development proceeds in these areas, we highly recommend that the vertical and 3-D distribution of SLCFs be evaluated, as that information is critical to improving the uncertain processes in models.

Inspired by the immense success of deep neural network in image processing and object recognition, learning-based image super resolution (SR) methods have been highly valued and have become the mainstream direction of super resolution research. Base on the recent proposed state-of-art convolution neural network (CNN) super-resolution methods, this paper proposed a generative adversarial network for single satellite image Super Resolution reconstruction. It built on a trained deep residual network to generate preliminary SR images, combined with a discriminative network learns to differentiate preliminary SR images and High resolution samples. The experiments results show that our method can use existing model parameters to refine SR image performance.

The Huangmaohai estuary (HE) is a funnel-shaped microtidal estuary in the west of the Pearl River Delta (PRD) in southern China. Since China reformed and opened up in 1978, extensive human activities have occurred and greatly changed the estuary's topography and modified its hydrodynamics. In this study, we examined the morphological evolution by analyzing remote sensing data with ArcGIS tools and studied the responses of hydrodynamics to the changes in topography from 1977 to 2010 by using the Delft3D model. We took the changes in estuarine circulation during neap tides in dry seasons as an example. The results show that human reclamation caused a narrowing of the estuary, and channel dredging deepened the estuary. These human activities changed both the longitudinal and lateral estuarine circulations. The longitudinal circulation was observed to increase with the deepening and narrowing of the estuary. The lateral circulation experienced changes in both the magnitude and pattern. The momentum balance analysis shows that when the depth and width changed simultaneously, the longitudinal estuarine circulation was modulated by both the channel deepening and width reduction, in which the friction, pressure gradient force, and advection terms were altered. The analysis of the longitudinal vortex dynamics indicates that the changes in the vertical shear of the longitudinal flow, lateral salinity gradient, and vertical mixing were responsible for the change in the lateral circulation. The changes in water depth are the dominant factors affecting lateral circulation intensity. This study has implications for sediment transport and morphological evolution in estuaries heavily impacted by human interventions.

The vertical distribution of aerosol extinction coefficient (EC) measured by lidar systems has been used to retrieve the profile of particle matter with a diameter µm (PM2.5). However, the traditional linear model (LM) cannot consider the influence of multiple meteorological variables sufficiently and then induce the low inversion accuracy. Generally, the machine learning (ML) algorithms can input multiple features which may provide us with a new way to solve this constraint. In this study, the surface aerosol EC and meteorological data from January 2014 to December 2017 were used to explore the conversion of aerosol EC to PM2.5 concentrations. Four ML algorithms were used to train the PM2.5 prediction models: random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and extreme gradient boosting decision tree (XGB). The mean absolute error (root mean square error) of LM, RF, KNN, SVM and XGB models were 11.66 (15.68), 5.35 (7.96), 7.95 (11.54), 6.96 (11.18) and 5.62 (8.27) µg/m3, respectively. This result shows that the RF model is the most suitable model for PM2.5 inversions from EC and meteorological data. Moreover, the sensitivity analysis of model input parameters was also conducted. All these results further indicated that it is necessary to consider the effect of meteorological variables when using EC to retrieve PM2.5 concentrations. Finally, the diurnal and seasonal variations of transport flux (TF) and PM2.5 profiles were analyzed based on the lidar data. The large PM2.5 concentration occurred at approximately 13:00–17:00 local time (LT) in 0.2–0.8 km. The diurnal variations of the TF show a clear conveyor belt at approximately 12:00–18:00 LT in 0.5–0.8 km. The results indicated that air pollutant transport over Wuhan mainly occurs at approximately 12:00–18:00 LT in 0.5–0.8 km. The TF near the ground usually has the highest value in winter (0.26 mg/m2 s), followed by the autumn and summer (0.2 and 0.19 mg/m2 s, respectively), and the lowest value in spring (0.14 mg/m2 s). These findings give us important information on the atmospheric profile and provide us sufficient confidence to apply lidar in the study of air quality monitoring.

Radiosonde (RS) is widely used to detect the vertical structures of the planetary boundary layer (PBL), and numerous methods have been proposed for retrieving PBL height (PBLH) from RS data. However, an algorithm that is suitable under all atmospheric conditions does not exist. This study evaluates the performance of four common PBLH algorithms under different thermodynamic stability conditions based on RS data collected from nine sites in January–December 2019. The four RS algorithms are the potential temperature gradient method (GMθ), relative humidity (RH) gradient method (GMRH), parcel method (PM) and Richardson number method (RM). Atmospheric conditions are divided into convective boundary layer (CBL), neutral boundary layer (NBL) and stable boundary layer (SBL) on the basis of the potential temperature profile. Results indicate that SBL is dominant at nighttime, whilst CBL dominates at daytime. Under all and SBL classifications, PBLH retrieved by RM is typically higher than those retrieved using the other methods. On the contrary, the PBLH result retrieved by PM is the lowest. Under CBL and NBL classifications, PBLH retrieved by PM is the highest. PBLH retrieved by GMθ and GMRH is relatively low under all classifications. Moreover, the uncertainty analysis shows that the consistency of PBLH retrieved by different algorithms is more than 80 % under CBL and NBL classifications. By contrast, the consistency of PBLH is less than 60 % under SBL classification. The average profiles and standard deviations of wind speed and potential temperature under consistent and inconsistent conditions are also investigated. The results indicate that consistent cases are typically accompanied by evident atmospheric stratification, such as a large gradient in the potential temperature profile or a low-level jet in the wind speed profile. These results indicate that the reliability of the PBLH results retrieved from RS data is affected by the structure of the boundary layer. Overall, GMθ and RM are appropriate for CBL condition. GMθ and PM are recommended for NBL condition. GMθ and GMRH are robust for SBL condition. This comprehensive comparison provides a reference for selecting the appropriate algorithm when retrieving PBLH from RS data.

Satellite-based cloud base and top height (CBH and CTH) and cloud geometrical thickness (CGT) are validated against ground-based lidar measurements and provide new scientific insights. The satellite measurements are done by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The retrieval methodology is built on the 333 m resolution low-level water cloud data obtained from the Vertical Feature Mask product of CALIPSO. The methodology is based on the definition that CBH of boundary layer clouds is the lowest cloud base over an area of several tens of kilometers. This allows taking the CBH of the neighboring penetrable shallower cloud as having CBH representative for the entire cloud field. The methodology over the ocean was validated based on observations from two surface-based ceilometer measurements in the islands of Barbados and the Azores, with an error standard deviation of ±115 m. Validation over land was based on 4 years data of 138 terrestrial ceilometer sites with an error standard deviation of ±220 m. The unprecedented accurate CBH allows us to obtain CGT, which is an essential parameter in the understanding of the aerosol–cloud interaction. Based on this newly developed methodology, we retrieved the annual, seasonal, and diurnal distributions of global CBH, CTH, and CGT for two years and analyzed the variations of CBH and CTH over the ocean and land. Climatology of the annual mean cloud geometrical properties shows the following. (1) The lowest CBH occurs over the eastern margins of the subtropical oceans and increases westward from 300–400 to 800–900 m. The CGT increases from 300 to 1200 m, respectively. In the western part of the tropical oceans, CBH is 500–600 m and CGT is ∼ 1500 m. (2) A narrow band of lower CBH and CGT occurs over the Equator, especially over the eastern parts of the oceans. (3) CBH and CGT over the tropical rain forests (Amazon and Congo) are 1200 and 1500 m, respectively. CBH over the drier tropical land is 1500–2000 m, with CGT of 800–1000 m. (4) CBH decreases towards Antarctica in the Southern Ocean, while CGT deepens. (5) Seasonally, the mid-latitude global oceans have the lowest CBH (mostly below 500 m) and CGT in summer seasons and the highest values in winter. (6) There is just an obvious difference between day and night for the maximum CTH and CGT over the tropics. Over the ocean, there is no discernible difference in CBH, but during night CTH is higher by ∼ 300 m.

The influence of both anthropogenic and forest-fire emissions, and their subsequent chemical and physical processing, on the accuracy of weather and air-quality forecasts, was studied using a high-resolution, online coupled air-quality model. Simulations were carried out for the period 4 July through 5 August 2019, at 2.5 km horizontal grid cell size, over a 2250×3425 km2 domain covering western Canada and USA, prior to the use of the forecast system as part of the FIREX-AQ ensemble forecast. Several large forest fires took place in the Canadian portion of the domain during the study period. A feature of the implementation was the incorporation of a new online version of the Canadian Forest Fire Emissions Prediction System (CFFEPSv4.0). This inclusion of thermodynamic forest-fire plume-rise calculations directly into the online air-quality model allowed us to simulate the interactions between forest-fire plume development and weather. Incorporating feedbacks resulted in weather forecast performance that exceeded or matched the no-feedback forecast, at greater than 90 % confidence, at most times and heights in the atmosphere. The feedback forecast outperformed the feedback forecast at 35 out of 48 statistical evaluation scores, for PM2.5, NO2, and O3. Relative to the climatological cloud condensation nuclei (CCN) and aerosol optical properties used in the no-feedback simulations, the online coupled model's aerosol indirect and direct effects were shown to result in feedback loops characterized by decreased surface temperatures in regions affected by forest-fire plumes, decreases in stability within the smoke plume, increases in stability further aloft, and increased lower troposphere cloud droplet and raindrop number densities. The aerosol direct and indirect effect reduced oceanic cloud droplet number densities and increased oceanic raindrop number densities, relative to the no-feedback climatological simulation. The aerosol direct and indirect effects were responsible for changes to the near-surface PM2.5 and NO2 concentrations at greater than the 90 % confidence level near the forest fires, with O3 changes remaining below the 90 % confidence level. The simulations show that incorporating aerosol direct and indirect effect feedbacks can significantly improve the accuracy of weather and air-quality forecasts and that forest-fire plume-rise calculations within an online coupled model change the predicted fire plume dispersion and emissions, the latter through changing the meteorology driving fire intensity and fuel consumption.

Aeolus is the first satellite mission to directly observe wind profile information on a global scale. After implementing a set of bias corrections, the Aeolus data products went public on 12 May 2020. However, Aeolus wind products over China have thus far not been evaluated extensively by ground-based remote sensing measurements. In this study, the Mie-cloudy and Rayleigh-clear wind products from Aeolus measurements are validated against wind observations from the radar wind profiler (RWP) network in China. Based on the position of each RWP site relative to the closest Aeolus ground tracks, three matchup categories are proposed, and comparisons between Aeolus wind products and RWP wind observations are performed for each category separately. The performance of Mie-cloudy wind products does not change much between the three matchup categories. On the other hand, for Rayleigh-clear and RWP wind products, categories 1 and 2 are found to have much smaller differences compared with category 3. This could be due to the RWP site being sufficiently approximate to the Aeolus ground track for categories 1 and 2. In the vertical, the Aeolus wind products are similar to the RWP wind observations, except for the Rayleigh-clear winds in the height range of 0–1 km. The mean absolute normalized differences between the Mie-cloudy (Rayleigh-clear) and the RWP wind components are 3.06 (5.45), 2.79 (4.81), and 3.32 (5.72) m/s at all orbit times and ascending and descending Aeolus orbit times, respectively. This indicates that the wind products for ascending orbits are slightly superior to those for descending orbits, and the observation time has a minor effect on the comparison. From the perspective of spatial differences, the Aeolus Mie-cloudy winds are consistent with RWP winds in most of east China, except in coastal areas where the Aeolus Rayleigh-clear winds are more reliable. Overall, the correlation coefficient R between the Mie-cloudy (Rayleigh-clear) wind and RWP wind component observation is 0.94 (0.81), suggesting that Aeolus wind products are in good agreement with wind observations from the RWP network in China. The findings give us sufficient confidence in assimilating the newly released Aeolus wind products in operational weather forecasting in China.

The Wuhan mesosphere–stratosphere–troposphere (MST) radar is a 53.8 MHz monostatic Doppler radar, located in Chongyang, Hubei Province, China, and has the capability to observe the dynamics of the mesosphere–stratosphere–troposphere region in the subtropical latitudes. The radar system has an antenna array of 576 Yagi antennas, and the maximum peak power is 172 kW. The Wuhan MST radar is efficient and cost-effective and employs more simplified and more flexible architecture. It includes 24 big transmitter–receiver (TR) modules, and the row or column data port of each big TR module connects 24 small TR modules via the corresponding row or column feeding network. Each antenna is driven by a small TR module with peak output power of 300 W. The arrangement of the antenna field, the functions of the timing signals, the structure of the TR modules, and the clutter suppression procedure are described in detail in this paper. We compared the MST radar observation results with other instruments and related models in the whole MST region for validation. Firstly, we made a comparison of the horizontal winds in the troposphere and low stratosphere observed by the Wuhan MST radar with the radiosonde on 22 May 2016, as well as with the ERA-Interim data sets (2016 and 2017) in the long term. Then, we made a comparison of the observed horizontal winds in the mesosphere with the meteor radar and the Horizontal Wind Model 14 (HWM-14) model in the same way. In general, good agreements can be obtained, and this indicates that the Wuhan MST is an effective tool to measure the three-dimensional wind fields of the MST region.

Wind profiles are fundamental to the research and applications in boundary layer meteorology, air quality and numerical weather prediction. Large-scale wind profile data have been previously documented from network observations in several countries, such as Japan, the USA, various European countries and Australia, but nationwide wind profiles observations are poorly understood in China. In this study, the salient characteristics and performance of wind profiles as observed by the radar wind profiler network of China are investigated. This network consists of more than 100 stations instrumented with 1290 MHz Doppler radar designed primarily for measuring vertically resolved winds at various altitudes but mainly in the boundary layer. It has good spatial coverage, with much denser sites in eastern China. The wind profiles observed by this network can provide the horizontal wind direction, horizontal wind speed and vertical wind speed for every 120 m interval within the height of 0 to 3 km. The availability of the radar wind profiler network has been investigated in terms of effective detection height, data acquisition rate, data confidence and data accuracy. Further comparison analyses with reanalysis data indicate that the observation data at 89 stations are recommended and 17 stations are not recommended. The boundary layer wind profiles from China can provide useful input to numerical weather prediction systems at regional scales.

Full-waveform light detection and ranging (LiDAR) has become a well-established technique for remote sensing of surface topography and target characterization, offering numerous opportunities in investigation and interpretation of the structural diversity of surface coverage. However, most prevailing waveform decomposition methods employ the Gaussian function or some other probability distributions to model LiDAR waveforms of specific shapes, which cannot be universally used. Moreover, most of these waveform decomposition methods operate at a single laser wavelength and cannot be applied well to multispectral LiDAR (MSL) or hyperspectral LiDAR (HSL), which can simultaneously collect the spectral and geometric attributes with multiple transmitting laser wavelengths. In this paper, we propose a new multispectral B-spline waveform decomposition model to achieve high precision multi-target ranging. Considering both the spatial consistency of each channel and the irregular shape of the received waveform, LiDAR waveforms are modelled by B-splines rather than any other specific probability distribution. Thus, the proposed method can be extended to other FWMSL data benefiting from the flexibility of B-splines on fitting arbitrary curves. Both simulated MSL echoes and a measured dataset from a FWMSL system with three wavelengths of 556, 670, and 780 nm were used in this study. Compared with two single wavelength waveform decomposition models and a multispectral waveform decomposition model based on Gaussian function, the proposed method has excellent robustness for processing different shapes of waveforms and can improve ranging accuracy significantly for irregular waveforms.

Temporal and spatial changes of aerosols characteristics was investigated to improve understandings of haze in winters of Wuhan, based on measurements of a CIMEL sun-sky radiometer and a modified Dark Target (DT) method. A large increase in no-absorbing and fine-mode particle is the main characteristic of aerosol during winter haze periods. From no-haze to haze conditions, aerosol optical depth (AOD), fine-mode peak value of size distribution, and single scattering albedo increased respectively from 0.46, 0.47 µm3/µm2, and 0.84 to 0.93, 0.91 µm3/µm2, and 0.87. Based on above aerosol optical and microphysical properties, we re-calculated aerosol models and improved the DT method. Results showed that our improvement increased the expect error (%) from 17.9 to 70.9. Spatial distributions revealed that heavy aerosol loading often occurred in rural and suburban areas rather than city centre. With the haze developing, the AOD increased by ∼0.3 over north of Wuhan and showed a downward trend from north to south.

Precise point cloud classification can enhance lidar performance in various applications, such as land cover mapping, forestry management and autonomous driving. The development of multispectral lidar improves classification performance with rich spectral information. However, the employment of spectral information for classification is still underdeveloped. Therefore, we proposed a spectrally improved classification method for multispectral LiDAR. We conducted spectral improvement in two aspects: (1) we improved the eigenentropy-based neighbourhood selection by spectral angle match (SAM) to reform the more reliable neighbour; (2) we utilized both geometric and spectral features and compare the contributions of these features. A three-wavelength multispectral lidar and a complex indoor experimental scene were used for demonstration. The results indicate the effectiveness of our proposed spectrally improved method and the promising potential of spectral information on lidar classification.

Abstract Modifications such as degrading the retrieval quality of mixed pixels in the coastline area and revising surface characterization scheme have been made to the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) Dark Target (DT) aerosol optical depth (AOD) product to address inaccuracy over urban areas. In this study, comprehensive evaluations of modifications to the MODIS C6.1 DT AOD product, in comparison with Collection 6 (C6), are conducted over mainland China under different spatial distributions, seasons, and air quality for the period 2010–2017, combined with validation against Aerosol Robotic Network (AERONET) AOD measurements. The preliminary result showed that the C6.1 DT AOD product in China displayed an overall good performance with high R2 (0.87) and low root‐mean‐square error (0.23) against ground measurements. Moreover, the C6.1 DT AOD product was an overall improvement over C6, with greater correlation and lower uncertainty against ground measurements, especially for the North China Plain and Central China, although this was not the case for Western China. The improvement was also seasonal, being distinct in spring but less pronounced in winter, and negatively correlated with the level of air pollution. Furthermore, the analysis of DT AOD retrievals in different urbanized areas illustrated that the updated DT algorithm worked well in completely urbanized areas, where 97% of C6.1 DT AOD retrievals were an improvement over C6, while approximately 10% of C6.1 DT AOD retrievals were deteriorated in semiurbanized areas. Additionally, the DT AOD retrievals in areas with relatively low enhanced vegetation index and high surface reflectance were significantly improved, mitigating problems associated with the DT algorithm, further improving the reliability of MODIS DT AOD products.

Atmospheric dimethyl sulfide, DMS(g), is a climatically important sulfur compound and is the main source of biogenic sulfate aerosol in the Arctic atmosphere. DMS(g) production and emission to the atmosphere increase during the summer due to the greater ice-free sea surface and higher biological activity. We implemented DMS(g) in the Environment and Climate Change Canada’s (ECCC) online air quality forecast model, GEM-MACH (Global Environmental Multiscale–Modelling Air quality and CHemistry), and compared model simulations with DMS(g) measurements made in Baffin Bay and the Canadian Arctic Archipelago in July and August 2014. Two seawater DMS(aq) datasets were used as input for the simulations: (1) a DMS(aq) climatology dataset based on seawater concentration measurements (Lana et al., 2011) and (2) a DMS(aq) dataset based on satellite detection (Galí et al., 2018). In general, GEM-MACH simulations under-predict DMS(g) measurements, which is likely due to the negative biases in both DMS(aq) datasets. However, a higher correlation and smaller bias were obtained with the satellite dataset. Agreement with the observations improved when climatological values were replaced by DMS(aq) in situ values that were measured concurrently with atmospheric observations over Baffin Bay and the Lancaster Sound area in July 2014. The addition of DMS(g) to the GEM-MACH model resulted in a significant increase in atmospheric SO2 for some regions of the Canadian Arctic (up to 100 %). Analysis of the size-segregated sulfate aerosol in the model shows that a significant increase in sulfate mass occurs for particles with a diameter smaller than 200 nm due to the formation and growth of biogenic aerosol at high latitudes (>70∘ N). The enhancement in sulfate particles is most significant in the size range from 50 to 100 nm; however, this enhancement is stronger in the 200–1000 nm size range at lower latitudes (∘ N). These results emphasize the important role of DMS(g) in the formation and growth of fine and ultrafine sulfate-containing particles in the Arctic during the summertime.

Multi-year observations of aerosol microphysical and optical properties, obtained through ground-based remote sensing at 50 China Aerosol Remote Sensing Network (CARSNET) sites, were used to characterize the aerosol climatology for representative remote, rural, and urban areas over China to assess effects on climate. The annual mean effective radii for total particles (ReffT) decreased from north to south and from rural to urban sites, and high total particle volumes were found at the urban sites. The aerosol optical depth at 440 nm (AOD440 nm) increased from remote and rural sites (0.12) to urban sites (0.79), and the extinction Ångström exponent (EAE440–870 nm) increased from 0.71 at the arid and semi-arid sites to 1.15 at the urban sites, presumably due to anthropogenic emissions. Single-scattering albedo (SSA440 nm) ranged from 0.88 to 0.92, indicating slightly to strongly absorbing aerosols. Absorption AOD440 nm values were 0.01 at the remote sites versus 0.07 at the urban sites. The average direct aerosol radiative effect (DARE) at the bottom of atmosphere increased from the sites in the remote areas (−24.40 W m−2) to the urban areas (−103.28 W m−2), indicating increased cooling at the latter. The DARE for the top of the atmosphere increased from −4.79 W m−2 at the remote sites to −30.05 W m−2 at the urban sites, indicating overall cooling effects for the Earth–atmosphere system. A classification method based on SSA440 nm, fine-mode fraction (FMF), and EAE440–870 nm showed that coarse-mode particles (mainly dust) were dominant at the rural sites near the northwestern deserts, while light-absorbing, fine-mode particles were important at most urban sites. This study will be important for understanding aerosol climate effects and regional environmental pollution, and the results will provide useful information for satellite validation and the improvement of climate modelling.

Motivated by the need to predict how the Arctic atmosphere will change in a warming world, this article summarizes recent advances made by the research consortium NETCARE (Network on Climate and Aerosols: Addressing Key Uncertainties in Remote Canadian Environments) that contribute to our fundamental understanding of Arctic aerosol particles as they relate to climate forcing. The overall goal of NETCARE research has been to use an interdisciplinary approach encompassing extensive field observations and a range of chemical transport, earth system, and biogeochemical models. Several major findings and advances have emerged from NETCARE since its formation in 2013. (1) Unexpectedly high summertime dimethyl sulfide (DMS) levels were identified in ocean water (up to 75 nM) and the overlying atmosphere (up to 1 ppbv) in the Canadian Arctic Archipelago (CAA). Furthermore, melt ponds, which are widely prevalent, were identified as an important DMS source (with DMS concentrations of up to 6 nM and a potential contribution to atmospheric DMS of 20 % in the study area). (2) Evidence of widespread particle nucleation and growth in the marine boundary layer was found in the CAA in the summertime, with these events observed on 41 % of days in a 2016 cruise. As well, at Alert, Nunavut, particles that are newly formed and grown under conditions of minimal anthropogenic influence during the months of July and August are estimated to contribute 20 % to 80 % of the 30–50 nm particle number density. DMS-oxidation-driven nucleation is facilitated by the presence of atmospheric ammonia arising from seabird-colony emissions, and potentially also from coastal regions, tundra, and biomass burning. Via accumulation of secondary organic aerosol (SOA), a significant fraction of the new particles grow to sizes that are active in cloud droplet formation. Although the gaseous precursors to Arctic marine SOA remain poorly defined, the measured levels of common continental SOA precursors (isoprene and monoterpenes) were low, whereas elevated mixing ratios of oxygenated volatile organic compounds (OVOCs) were inferred to arise via processes involving the sea surface microlayer. (3) The variability in the vertical distribution of black carbon (BC) under both springtime Arctic haze and more pristine summertime aerosol conditions was observed. Measured particle size distributions and mixing states were used to constrain, for the first time, calculations of aerosol–climate interactions under Arctic conditions. Aircraft- and ground-based measurements were used to better establish the BC source regions that supply the Arctic via long-range transport mechanisms, with evidence for a dominant springtime contribution from eastern and southern Asia to the middle troposphere, and a major contribution from northern Asia to the surface. (4) Measurements of ice nucleating particles (INPs) in the Arctic indicate that a major source of these particles is mineral dust, likely derived from local sources in the summer and long-range transport in the spring. In addition, INPs are abundant in the sea surface microlayer in the Arctic, and possibly play a role in ice nucleation in the atmosphere when mineral dust concentrations are low. (5) Amongst multiple aerosol components, BC was observed to have the smallest effective deposition velocities to high Arctic snow (0.03 cm s−1).

The plastic deformation of the AlSi10Mg alloy manufactured via laser powder bed fusion (LPBF) is incompatible at the microscale, which causes residual strains/stresses and dislocation pile-ups at the Al/Si interfaces and grain boundaries. Hence, it is of fundamental significance to clarify these microscopic properties during plastic deformation. Here, in-situ neutron diffraction is employed to explore the residual strains, stresses, and dislocation density in the LPBF AlSi10Mg during loading-unloading-reloading deformation. It is found that the maximum residual stresses of the Al and Si phases in the loading direction reach up to about −115 (compressive) and 832 (tensile) MPa, respectively. A notable dislocation annihilation phenomenon is observed in the Al matrix: the dislocation density decreases significantly during unloading stages, and the amplitude of this reduction increases after experiencing a larger plastic deformation. At the macroscale, this dislocation annihilation phenomenon is associated with the reverse strain after unloading. At the microscale, the annihilation phenomenon is driven by the compressive residual stress in the Al matrix. Meanwhile, the annihilation of screw dislocations during unloading stages contributes to the reduction in total dislocation density.

A first regional assessment of the impact of shipping emissions on air pollution in the Canadian Arctic and northern regions was conducted in this study. Model simulations were carried out on a limited-area domain (at 15 km horizontal resolution) centred over the Canadian Arctic, using the Environment and Climate Change Canada's on-line air quality forecast model, GEM-MACH (Global Environmental Multi-scale – Modelling Air quality and CHemistry), to investigate the contribution from the marine shipping emissions over the Canadian Arctic waters (at both present and projected future levels) to ambient concentrations of criteria pollutants (O3, PM2.5, NO2, and SO2), atmospheric deposition of sulfur (S) and nitrogen (N), and atmospheric loading and deposition of black carbon (BC) in the Arctic. Several model upgrades were introduced for this study, including the treatment of sea ice in the dry deposition parameterization, chemical lateral boundary conditions, and the inclusion of North American wildfire emissions. The model is shown to have similar skills in predicting ambient O3 and PM2.5 concentrations in the Canadian Arctic and northern regions, as the current operational air quality forecast models in North America and Europe. In particular, the model is able to simulate the observed O3 and PM components well at the Canadian high Arctic site, Alert. The model assessment shows that, at the current (2010) level, Arctic shipping emissions contribute to less than 1 % of ambient O3 concentration over the eastern Canadian Arctic and between 1 and 5 % of ambient PM2.5 concentration over the shipping channels. Arctic shipping emissions make a much greater contributions to the ambient NO2 and SO2 concentrations, at 10 %–50 % and 20 %–100 %, respectively. At the projected 2030 business-as-usual (BAU) level, the impact of Arctic shipping emissions is predicted to increase to up to 5 % in ambient O3 concentration over a broad region of the Canadian Arctic and to 5 %–20 % in ambient PM2.5 concentration over the shipping channels. In contrast, if emission controls such as the ones implemented in the current North American Emission Control Area (NA ECA) are to be put in place over the Canadian Arctic waters, the impact of shipping to ambient criteria pollutants would be significantly reduced. For example, with NA-ECA-like controls, the shipping contributions to the population-weighted concentrations of SO2 and PM2.5 would be brought down to below the current level. The contribution of Canadian Arctic shipping to the atmospheric deposition of sulfur and nitrogen is small at the current level,

Plume rise parameterizations calculate the rise of pollutant plumes due to effluent buoyancy and exit momentum. Some form of these parameterizations is used by most air quality models. In this paper, the performance of the commonly used Briggs plume rise algorithm was extensively evaluated, through a comparison of the algorithm's results when driven by meteorological observations with direct observations of plume heights in the Athabasca oil sands region. The observations were carried out as part of the Canada-Alberta Joint Oil Sands Monitoring Plan in August and September of 2013. Wind and temperature data used to drive the algorithm were measured in the region of emissions from various platforms, including two meteorological towers, a radio-acoustic profiler, and a research aircraft. Other meteorological variables used to drive the algorithm include friction velocity, boundary-layer height, and the Obukhov length. Stack emissions and flow parameter information reported by continuous emissions monitoring systems (CEMSs) were used to drive the plume rise algorithm. The calculated plume heights were then compared to interpolated aircraft SO2 measurements, in order to evaluate the algorithm's prediction for plume rise. We demonstrate that the Briggs algorithm, when driven by ambient observations, significantly underestimated plume rise for these sources, with more than 50 % of the predicted plume heights falling below half the observed values from this analysis. With the inclusion of the effects of effluent momentum, the choice of different forms of parameterizations, and the use of different stability classification systems, this essential finding remains unchanged. In all cases, approximately 50 % or more of the predicted plume heights fall below half the observed values. These results are in contrast to numerous plume rise measurement studies published between 1968 and 1993. We note that the observations used to drive the algorithms imply the potential presence of significant spatial heterogeneity in meteorological conditions; we examine the potential impact of this heterogeneity in our companion paper (Akingunola et al., 2018). It is suggested that further study using long-term in situ measurements with currently available technologies is warranted to investigate this discrepancy, and that wherever possible, meteorological input variables are observed in the immediate vicinity of the emitting stacks.

The atmospheric boundary layer is an important atmospheric feature that affects environmental health and weather forecasting. In this study, we proposed a graphics algorithm for the derivation of atmospheric boundary layer height (BLH) from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data. Owing to the differences in scattering intensity between molecular and aerosol particles, the total attenuated backscatter coefficient 532 and attenuated backscatter coefficient 1064 were used simultaneously for BLH detection. The proposed algorithm transformed the gradient solution into graphics distribution solution to overcome the effects of large noise and improve the horizontal resolution. This method was then tested with real signals under different horizontal smoothing numbers (1, 3, 15 and 30). Finally, the results of BLH obtained by CALIPSO data were compared with the results retrieved by the ground-based lidar measurements. Under the horizontal smoothing number of 15, 12 and 9, the correlation coefficients between the BLH derived by the proposed algorithm and ground-based lidar were both 0.72. Under the horizontal smoothing number of 6, 3 and 1, the correlation coefficients between the BLH derived by graphics distribution method (GDM) algorithm and ground-based lidar were 0.47, 0.14 and 0.12, respectively. When the horizontal smoothing number was large (15, 12 and 9), the CALIPSO BLH derived by the proposed method demonstrated a good correlation with ground-based lidar. The algorithm provided a reliable result when the horizontal smoothing number was greater than 9. This finding indicated that the proposed algorithm can be applied to the CALIPSO satellite data with 3 and 5 km horizontal resolution.

Environment and Climate Change Canada's online air quality forecasting model, GEM-MACH, was extended to simulate atmospheric concentrations of benzene and seven polycyclic aromatic hydrocarbons (PAHs): phenanthrene, anthracene, fluoranthene, pyrene, benz(a)anthracene, chrysene, and benzo(a)pyrene. In the expanded model, benzene and PAHs are emitted from major point, area, and mobile sources, with emissions based on recent emission factors. Modelled PAHs undergo gas–particle partitioning (whereas benzene is only in the gas phase), atmospheric transport, oxidation, cloud processing, and dry and wet deposition. To represent PAH gas–particle partitioning, the Dachs–Eisenreich scheme was used, and we have improved gas–particle partitioning parameters based on an empirical analysis to get significantly better gas–particle partitioning results than the previous North American PAH model, AURAMS-PAH. Added process parametrizations include the particle phase benzo(a)pyrene reaction with ozone via the Kwamena scheme and gas-phase scavenging of PAHs by snow via vapour sorption to the snow surface.The resulting GEM-MACH-PAH model was used to generate the first online model simulations of PAH emissions, transport, chemical transformation, and deposition for a high-resolution domain (2.5 km grid cell spacing) in North America, centred on the PAH data-rich region of southern Ontario, Canada and the northeastern US. Model output for two seasons was compared to measurements from three monitoring networks spanning Canada and the US Average spring–summertime model results were found to be statistically unbiased from measurements of benzene and all seven PAHs. The same was true for the fall–winter seasonal mean, except for benzo(a)pyrene, which had a statistically significant positive bias. We present evidence that the benzo(a)pyrene results may be ameliorated via further improvements to particulate matter and oxidant processes and transport. Our analysis focused on four key components to the prediction of atmospheric PAH levels: spatial variability, sensitivity to mobile emissions, gas–particle partitioning, and wet deposition. Spatial variability of PAHs ∕ PM2.5 at a 2.5 km resolution was found to be comparable to measurements. Predicted ambient surface concentrations of benzene and the PAHs were found to be critically dependent on mobile emission factors, indicating the mobile emissions sector has a significant influence on ambient PAH levels in the study region. PAH wet deposition was overestimated due to additive precipitation biases in the model and the measurements. Our overall performance evaluation suggests that GEM-MACH-PAH can provide seasonal estimates for benzene and PAHs and is suitable for emissions scenario simulations.

Salt intrusion in the Pearl River estuary (PRE) is a dynamic process that is influenced by a range of factors and to date, few studies have examined the effects of winds and waves on salt intrusion in the PRE. We investigate these effects using the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system applied to the PRE. After careful validation, the model is used for a series of diagnostic simulations. It is revealed that the local wind considerably strengthens the salt intrusion by lowering the water level in the eastern part of the estuary and increasing the bottom landward flow. The remote wind increases the water mixing on the continental shelf, elevates the water level on the shelf and in the PRE and pumps saltier shelf water into the estuary by Ekman transport. Enhancement of the salt intrusion is comparable between the remote and local winds. Waves decrease the salt intrusion by increasing the water mixing. Sensitivity analysis shows that the axial down-estuary wind, is most efficient in driving increases in salt intrusion via wind straining effect.

Human teeth are the important masticatory organ and therefore are subjected to friction and wear everyday. Both the loading condition and the environment are relatively complex in the mouth, however, normally human teeth can serve in mouth all their time with excellent wear resistance in most people. Obviously, through the process of human evolution, human teeth have become a natural anti-wear system that far outclasses the best engineering anti-wear systems at present. To understand the excellent anti-wear properties of human teeth, we need to look into what act as the wear resisting elements of human teeth. In this paper, based on the results obtained by the authors and from literatures, the formation mechanism of dental anti-wear function was summarized. Particular attention was paid to the multi-scale anti-wear mechanism caused by the unique hierarchical structure of human teeth, the self-repair mechanism by tooth remineralization, and the cooperating lubrication mechanism of salivary pellicle upon tooth surface and the hydration proteins within the tooth. Based on the formation mechanism of dental anti-wear function, some bionic design perspectives were discussed and future research directions are recommended.

The automated building detection in aerial images is a fundamental problem encountered in aerial and satellite images analysis. Recently, thanks to the advances in feature descriptions, Region-based CNN model (R-CNN) for object detection is receiving an increasing attention. Despite the excellent performance in object detection, it is problematic to directly leverage the features of R-CNN model for building detection in single aerial image. As we know, the single aerial image is in vertical view and the buildings possess significant directional feature. However, in R-CNN model, direction of the building is ignored and the detection results are represented by horizontal rectangles. For this reason, the detection results with horizontal rectangle cannot describe the building precisely. To address this problem, in this paper, we proposed a novel model with a key feature related to orientation, namely, Oriented R-CNN (OR-CNN). Our contributions are mainly in the following two aspects: 1) Introducing a new oriented layer network for detecting the rotation angle of building on the basis of the successful VGG-net R-CNN model; 2) the oriented rectangle is proposed to leverage the powerful R-CNN for remote-sensing building detection. In experiments, we establish a complete and bran-new data set for training our oriented R-CNN model and comprehensively evaluate the proposed method on a publicly available building detection data set. We demonstrate State-of-the-art results compared with the previous baseline methods.

Ground-based lidar, working as an effective remote sensing tool, plays an irreplaceable role in the study of atmosphere, since it has the ability to provide the atmospheric vertical profile. However, the appearance of noise in a lidar signal is unavoidable, which leads to difficulties and complexities when searching for more information. Every de-noising method has its own characteristic but with a certain limitation, since the lidar signal will vary with the atmosphere changes. In this paper, a universal de-noising algorithm is proposed to enhance the SNR of a ground-based lidar signal, which is based on signal segmentation and reconstruction. The signal segmentation serving as the keystone of the algorithm, segments the lidar signal into three different parts, which are processed by different de-noising method according to their own characteristics. The signal reconstruction is a relatively simple procedure that is to splice the signal sections end to end. Finally, a series of simulation signal tests and real dual field-of-view lidar signal shows the feasibility of the universal de-noising algorithm.

Hyperspectral LiDAR (HSL) is a novel tool in the field of active remote sensing, which has been widely used in many domains because of its advantageous ability of spectrum-gained. Especially in the precise monitoring of nitrogen in green plants, the HSL plays a dispensable role. The exiting HSL system used for nitrogen status monitoring has a multi-channel detector, which can improve the spectral resolution and receiving range, but maybe result in data redundancy, difficulty in system integration and high cost as well. Thus, it is necessary and urgent to pick out the nitrogen-sensitive feature wavelengths among the spectral range. The present study, aiming at solving this problem, assigns a feature weighting to each centre wavelength of HSL system by using matrix coefficient analysis and divergence threshold. The feature weighting is a criterion to amend the centre wavelength of the detector to accommodate different purpose, especially the estimation of leaf nitrogen content (LNC) in rice. By this way, the wavelengths high-correlated to the LNC can be ranked in a descending order, which are used to estimate rice LNC sequentially. In this paper, a HSL system which works based on a wide spectrum emission and a 32-channel detector is conducted to collect the reflectance spectra of rice leaf. These spectra collected by HSL cover a range of 538 nm – 910 nm with a resolution of 12 nm. These 32 wavelengths are strong absorbed by chlorophyll in green plant among this range. The relationship between the rice LNC and reflectance-based spectra is modeled using partial least squares (PLS) and support vector machines (SVMs) based on calibration and validation datasets respectively. The results indicate that I) wavelength selection method of HSL based on feature weighting is effective to choose the nitrogen-sensitive wavelengths, which can also be co-adapted with the hardware of HSL system friendly. II) The chosen wavelength has a high correlation with rice LNC which can be retrieved by using PLS and SVMs regression methods.

Precision agriculture has always been the research hotspot around the world. And the optimization of nitrogen fertilization for crops is the core concerns. It is not only to improve the productivity of crops but also to avoid the environmental risks caused by over-fertilization. Therefore, accurate estimation of nitrogen status is crucial for determining an nitrogen recommendation. Remote sensing techniques have been widely used to monitor crops for years, and they could offer estimations for stress status diagnosis through obtaining vertical structure parameters and spectral reflectance properties of crops. As an active remote sensing technology, lidar is particularly attractive for 3-dimensional information at a high point density. It has unique edges in obtaining vertical structure parameters of crops. However, capability of spectral reflectance properties is what the current lidar technology lacks because of single wavelength detection. To solve this problem, the concept of novel hyperspectral lidar (HSL), which combines the advantages of hyperspectal reflectance with high 3-dimensional capability of lidar, was proposed in our study. The design of instrument was described in detail. A broadband laser pulse was emitted and reflectance spectrum with 32 channels could be detected. Furthermore, the experiment was carried out by the novel HSL system to testify the potential application for monitoring nitrogen stress. Rice under different levels of nitrogen fertilization in central China were selected as the object of study, and four levels of nitrogen fertilization (N1-N4) were divided. With the detection of novel lidar system, high precision structure parameters of crops could be provided. Meanwhile, spectral reflectance properties in 32 wavebands were also obtained. The high precision structure parameters could be used to evaluate the stress status of crops. And abundant spectral information in 32 wavebands could improve the capacity of lidar system significantly. The results demonstrate that it is more effective for HSL system to distinguish different levels of nitrogen fertilization. Overall, HSL allows for probing not only high precision structure parameters but also spectral reflectance properties of crops. Compared with other approaches, the novel HSL has the potential to provide more comprehensive information of crops which can be assessed remotely in the application of precision agriculture.

Differential absorption lidar (DIAL) remote sensing is a promising technology for atmospheric CO2 detection. However, stringent wavelength accuracy and stability are required in DIAL system. Accurate on-line wavelength calibration is a crucial procedure for retrieving atmospheric CO2 concentration using the DIAL, particularly when pulsed lasers are adopted in the system. Large fluctuations in the intensities of a pulsed laser pose a great challenge for accurate on-line wavelength calibration. In this paper, a wavelength calibration strategy based on multi-wavelength scanning (MWS) was proposed for accurate on-line wavelength calibration of a pulsed laser for CO2 detection. The MWS conducted segmented sampling across the CO2 absorption line with appropriate number of points and range of widths by using a tunable laser. Complete absorption line of CO2 can be obtained through a curve fitting. Then, the on-line wavelength can be easily found at the peak of the absorption line. Furthermore, another algorithm called the energy matching was introduced in the MWS to eliminate the backlash error of tunable lasers during the process of on-line wavelength calibration. Finally, a series of tests was conducted to elevate the calibration precision of MWS. Analysis of tests demonstrated that the MWS proposed in this paper could calibrate the on-line wavelength of pulsed laser accurately and steadily.

Laser-induced fluorescence (LIF) served as an active technology has been widely used in many field, and it is closely related to excitation wavelength (EW). The objective of this investigation is to discuss the performance of different EWs of LIF LiDAR in identifying plant species. In this study, the 355, 460 and 556 nm lasers were utilized to excite the leaf fluorescence and the fluorescence spectra were measured by using the LIF LiDAR system built in the laboratory. Subsequently, the principal component analysis (PCA) with the help of support vector machine (SVM) was utilized to analyse fluorescence spectra. For the three EWs, the overall identification rates of the six plant species were 80 %, 83.3 % and 90 %. Experimental results demonstrated that 556 nm excitation light source is superior to 355 and 460 nm for the classification of the plant species for the same genus in this study. Thus, an appropriate excitation wavelength should be considered when the LIF LiDAR was utilized in the field of remote sensing based on the LIF technology.

The accurate estimation of deposits adhering on insulators is critical to prevent pollution flashovers which cause huge costs worldwide. The traditional evaluation method of insulator contaminations (IC) is based sparse manual in-situ measurements, resulting in insufficient spatial representativeness and poor timeliness. Filling that gap, we proposed a novel evaluation framework of IC based on remote sensing and data mining. Varieties of products derived from satellite data, such as aerosol optical depth (AOD), digital elevation model (DEM), land use and land cover and normalized difference vegetation index were obtained to estimate the severity of IC along with the necessary field investigation inventory (pollution sources, ambient atmosphere and meteorological data). Rough set theory was utilized to minimize input sets under the prerequisite that the resultant set is equivalent to the full sets in terms of the decision ability to distinguish severity levels of IC. We found that AOD, the strength of pollution source and the precipitation are the top 3 decisive factors to estimate insulator contaminations. On that basis, different classification algorithm such as mahalanobis minimum distance, support vector machine (SVM) and maximum likelihood method were utilized to estimate severity levels of IC. 10-fold cross-validation was carried out to evaluate the performances of different methods. SVM yielded the best overall accuracy among three algorithms. An overall accuracy of more than 70% was witnessed, suggesting a promising application of remote sensing in power maintenance. To our knowledge, this is the first trial to introduce remote sensing and relevant data analysis technique into the estimation of electrical insulator contaminations.

Paddy rice is one of the most important cereal crops in China. Nitrogen (N) is closely related to crops production by influencing the photosynthetic efficiency of paddy rice. In this study, laser-induced fluorescence (LIF) technology with the help of principal component analysis (PCA) and back-propagation neural network (BPNN) is proposed to monitor leaf N content (LNC) of paddy rice. The PCA is utilized to extract the characteristic variables of LIF spectra by analysing the major attributes. The results showed that the first three principal components (PCs) can explain 95.76% and 93.53% of the total variance contained in the fluorescence spectra for tillering stage and shooting stage, respectively. Then, BPNN was utilized to inverse the LNC on the basis of the first three PCs as input variables and can obtain the satisfactory inversion results (R2 of tillering stage and shooting stage are 0.952 and 0.931, respectively; residual main range from -0.2 to 0.2 mg/g). The experimental results demonstrated that LIF technique combined with multivariate analysis will be a useful method for monitoring the LNC of paddy rice, which can provide consultations for the decision-making of peasants in their N fertilization strategies.

To identify accurately the doses of nitrogen (N) fertilizer and improve the photosynthetic efficiency of paddy rice, laser induced fluorescence (LIF) technique combined with the support vector machine (SVM) and principal component analysis (PCA) is proposed in this paper. The LIF technology, in which the ultraviolet light (355 nm) is applied as an excitation light source, is employed to measure fluorescence spectra of paddy rice. These fluorescence spectra demonstrate that the fluorescence spectral characteristics of paddy rice leaves with different doses of N fertilizer have distinct differences from each other. Then, PCA and SVM are implemented to extract the features of fluorescence spectra and to recognize different doses of N fertilizer, respectively. The overall recognition accuracy can reach 95%. The results show that the LIF technology combined with PCA and SVM is a convenient, rapid, and sensitive diagnostic method for detecting N levels of paddy rice. Thus, it will also be convenient for farmers to manage accurately their fertilization strategies.

Parameter specification usually has significant influence on the performance of land surface models (LSMs). However, estimating the parameters properly is a challenging task due to the following reasons: (1) LSMs usually have too many adjustable parameters (20 to 100 or even more), leading to the curse of dimensionality in the parameter input space; (2) LSMs usually have many output variables involving water/energy/carbon cycles, so that calibrating LSMs is actually a multi-objective optimization problem; (3) Regional LSMs are expensive to run, while conventional multi-objective optimization methods need a large number of model runs (typically ~105–106). It makes parameter optimization computationally prohibitive. An uncertainty quantification framework was developed to meet the aforementioned challenges, which include the following steps: (1) using parameter screening to reduce the number of adjustable parameters, (2) using surrogate models to emulate the responses of dynamic models to the variation of adjustable parameters, (3) using an adaptive strategy to improve the efficiency of surrogate modeling-based optimization; (4) using a weighting function to transfer multi-objective optimization to single-objective optimization. In this study, we demonstrate the uncertainty quantification framework on a single column application of a LSM – the Common Land Model (CoLM), and evaluate the effectiveness and efficiency of the proposed framework. The result indicate that this framework can efficiently achieve optimal parameters in a more effective way. Moreover, this result implies the possibility of calibrating other large complex dynamic models, such as regional-scale LSMs, atmospheric models and climate models.

Paddy rice is important for Chinese agriculture and crop production, which largely depends on the leaf nitrogen (N) levels. The purpose of this study is to discuss the relationship between the fluorescence parameters and leaf N content of paddy rice and to test their performance in inversing N content of crops through back-propagation (B-P) neural network. In the correlative analysis of the fluorescence parameters and the N content, we found that the correlation between fluorescence ratios (F740/F685 and F685/F525 (F740, F685, F525 - intensity of fluorescence at 740, 685 and 525 nm, respectively)) and the N content (R2 are 0.735 and 0.4342, respectively) is weaker than that between the intensity of fluorescence peaks (F685 and F740) and N content (R2 are 0.9743 and 0.9686, respectively). Our studies show that the accuracy and precision of N content inversion which is acquired from the intensity of fluorescence peaks through the B-P neural network model are significantly improved (root mean square error (MSRE) = 0.1702, the residual changes between -0.1-0.1 mg/g) compared with the fluorescence ratio (MSRE = 0.3655, the residual changes from -0.3-0.3 mg/g). Results demonstrate that the intensity of fluorescence peaks can be as a characteristic parameter to estimate N content of crops leaf. The B-P neural network model will be serviceable approach in inversing N content of paddy leaf.

Landfill leachate generated during the landfill process of municipal solid waste contains high concentration of pollutants and has adverse effects on the environment. Leachate is very difficult to be treated to meet the strict quality standards for direct discharge into surface water. Supercritical water gasification might provide an effective conversion method for leachate to hydrogen energy. Gasification of landfill leachate in supercritical water using batch-type reactor was investigated. The effect of temperature, pressure and retention time on gas component and COD, TOC removal efficiency was researched. Experiments were conducted under conditions of 380 – 500 °C, 22.5 – 37.5 MPa, 5 – 25 min. The experimental results showed that gaseous products mainly contain H2, CH4, CO2 and CO. The volume percentage of H2 reaching up to 55.2 % was obtained at 500 °C, 30 MPa and 10 min. Moreover, COD, TOC removal efficiency being 91.96 %, 84.81 % was obtained at 470 °C, 27 MPa, 25 min. Hydrogen component and COD, TOC removal efficiency increased with the increase of temperature. The suitable retention time is 25 min for leachate gasification in supercritical water. The main liquid intermediate organic products are caproic acid, propionic acid, valeric acid, butyric acid, carbolic acid and p- cresol etc.

Proper specification of model parameters is critical to the performance of land surface models (LSMs). Due to high dimensionality and parameter interaction, estimating parameters of an LSM is a challenging task. Sensitivity analysis (SA) is a tool that can screen out the most influential parameters on model outputs. In this study, we conducted parameter screening for six output fluxes for the Common Land Model: sensible heat, latent heat, upward longwave radiation, net radiation, soil temperature and soil moisture. A total of 40 adjustable parameters were considered. Five qualitative SA methods, including local, sum-of-trees, multivariate adaptive regression splines, delta test and Morris methods, were compared. The proper sampling design and sufficient sample size necessary to effectively screen out the sensitive parameters were examined. We found that there are 2–8 sensitive parameters, depending on the output type, and about 400 samples are adequate to reliably identify the most sensitive parameters. We also employed a revised Sobol' sensitivity method to quantify the importance of all parameters. The total effects of the parameters were used to assess the contribution of each parameter to the total variances of the model outputs. The results confirmed that global SA methods can generally identify the most sensitive parameters effectively, while local SA methods result in type I errors (i.e., sensitive parameters labeled as insensitive) or type II errors (i.e., insensitive parameters labeled as sensitive). Finally, we evaluated and confirmed the screening results for their consistency with the physical interpretation of the model parameters.

Remote Sensing plays a critical role in operational volcano monitoring due to the often remote locations of volcanic systems and the large spatial extent of potential eruption pre-cursor signals. Despite the all-weather capabilities of radar remote sensing and despite its high performance in monitoring change, the contribution of radar data to operational monitoring activities has been limited in the past. This is largely due to (1) the high data costs associated with radar data, (2) the slow data processing and delivery procedures, and (3) the limited temporal sampling provided by spaceborne radars. With this paper, we present new data processing and data integration techniques that mitigate some of the above mentioned limitations and allow for a meaningful integration of radar remote sensing data into operational volcano monitoring systems. The data integration concept presented here combines advanced data processing techniques with fast data access procedures in order to provide high quality radar-based volcano hazard information at improved temporal sampling rates. First performance analyses show that the integration of SAR can significantly improve the ability of operational systems to detect eruptive precursors. Therefore, the developed technology is expected to improve operational hazard detection, alerting, and management capabilities.