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Chemical losses of ambient reactive volatile organic compounds (VOCs) is a long-term issue yet to be resolved in VOC source apportionments. These losses substantially reduce the concentrations of highly reactive species in the apportioned factor profiles and result in the underestimation of source contributions. This review assesses the common methods and existing issues in ways to reduce losses and loss impacts in source analyses and suggests research directions for improved VOC source apportionments. Positive matrix factorization (PMF) is now the main VOC source analysis method compared to other mathematical models. The issue in using any apportionment tool is the processing of the data to be analyzed to reduce the impacts of reactive losses. Estimating the initial concentrations of ambient VOCs based on photochemical age has become the primary approach to reduce reactive loss effects in PMF, except for selecting low-reactivity species or nighttime data into the analysis. Currently, the initial concentration method only considers daytime reactions with hydroxyl (⚫OH) radicals. However, the ⚫OH rate constants vary with temperature, and that has not been considered. Losses from reactions with O3 and NO3 radicals, especially for alkene species, remain to be included. Thus, the accuracy of the photochemical age estimation is uncertain. Beyond developing accurate quantitative approaches for reactive losses, source analyses methods for the consumed VOCs and the accurate quantification of different source contributions to O3 and secondary organic aerosols are important additional directions for future research.

To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.

Traveling wave is an innovative active flow control technique that can remarkably mitigate flow separation. This paper employs numerical simulation to examine how traveling wave structures affect the NACA0012 airfoil. The traveling wave structure is situated at 0.5%c from the leading edge. In the chord direction, its projection length is 0.1c. Through numerical simulation, the impacts of dimensionless length-width ratio and velocity of traveling wave on flow separation are investigated, and the relationship between the traveling wave's optimal parameters and angle of attack is explored. The outcomes demonstrate that traveling waves with suitable length-width ratios and velocities can effectively suppress flow separation. When AoA=16°, traveling wave airfoil with dimensionless velocity U=1.1 and length-width ratio A=1 achieves the best performance, and its lift-drag ratio is 9.24 times that of the original NACA0012 airfoil. The optimal dimensionless length-width ratio and velocity of the traveling wave airfoil are associated with the angle of attack, and different parameters need to be chosen at various angles of attack to attain optimum effect.

Abstract Two-dimensional (2D) materials offer a prospect of membranes that combine negligible gas permeability with high proton conductivity and could outperform the existing proton exchange membranes used in various applications including fuel cells. Graphene oxide (GO), a well-known 2D material, facilitates rapid proton transport along its basal plane but proton conductivity across it remains unknown. It is also often presumed that individual GO monolayers contain a large density of nanoscale pinholes that lead to considerable gas leakage across the GO basal plane. Here we show that relatively large, micrometer-scale areas of monolayer GO are impermeable to gases, including helium, while exhibiting proton conductivity through the basal plane which is nearly two orders of magnitude higher than that of graphene. These findings provide insights into the key properties of GO and demonstrate that chemical functionalization of 2D crystals can be utilized to enhance their proton transparency without compromising gas impermeability.

A chemical transport model (CTM) is an essential tool for air quality prediction and management, widely used in air pollution control and health risk assessment. However, the current models do not perform very well in reproducing the observations of some major chemical components, for example, sulfate, nitrate, ammonium and organic carbon. Studies have suggested that the uncertainties in the model chemical mechanism, source emission inventory and meteorological field can cause inaccurate simulation results. Still, the emission source profile (used to create speciated emission inventories for CTMs) of PM2.5 has not been fully taken into account in current numerical simulation. Based on the characteristics and variation rules of chemical components in typical PM2.5 sources, different simulation scenarios were designed and the sensitivity of simulated PM2.5 components to the source chemical profile was explored. Our findings showed that the influence of source profile changes on simulated PM2.5 components' concentrations cannot be ignored. Simulation results of some components were sensitive to the adopted source profile in CTMs. Moreover, there was a linkage effect: the variation in some components in the source profile would bring changes to the simulated results of other components. These influences are connected to chemical mechanisms of the model since the variation in species allocations in emission sources can affect the potential composition and phase state of aerosols, chemical reaction priority, and multicomponent chemical balance in thermodynamic equilibrium systems. We also found that the perturbation of the PM2.5 source profile caused variation in simulated gaseous pollutants, which indirectly indicated that the perturbation of source profile would affect the simulation of secondary PM2.5 components. Our paper highlights the necessity of paying enough attention to the representativeness and timeliness of the source profile when using CTMs for simulation.

Understanding the effectiveness of long-term air pollution regulatory measures is important for control policy formulation. Efforts have been made using chemical transport modelling and statistical approaches to evaluate the efficacy of the Clean Air Action Plan (CAAP; 2013–2017) and the Blue Sky Protection Campaign (BSPC; 2018–2020) enacted in China. Changes in air quality due to reduction in emissions can be masked by meteorology, making it highly challenging to reveal the real effects of control measures. A knowledge gap still existed with respect to how sources changed before and after the CAAP and BSPC were implemented, respectively, particularly in coastal areas where anthropogenic emissions mixed with additional natural sources (e.g. marine aerosol). This work applied a machine-learning-based meteorological normalization approach to decouple the meteorological effects from air quality trend in a coastal city in northern China (Qingdao). Secondly, the relative changes in source contributions to ambient PM2.5 with a ∼ 10-year observation interval (2011–2012, 2016, and 2019) were also investigated. We discovered that the largest emission reduction section was likely from coal combustion as the meteorologically normalized SO2 dropped by ∼ 15.5 % yr−1, and the annual average dispersion-normalized SO42- decreased by ∼ 41.5 %. Change in the meteorologically normalized NO2 was relatively stable (∼ 1.0 % yr−1), and NO3- changed inappreciably in 2016–2019 but was significantly higher than that prior to the CAAP. Crustal dust decreased remarkably after the CAAP began. Industrial emissions, for example, steel-related smelting, decreased after 2016 due to the relocation of steel-making enterprises. Note that vehicle emissions were increased in importance as opposed to the other primary sources. Similar to other megacities, Qingdao is also at risk of increased ozone pollution that in turn facilitates secondary-particle formation in the future. The policy assessment approaches applied in this work also work for other places where air quality management is highly in demand to reduce air pollution.

Semantic segmentation algorithms based on full convolutional neural network have greatly improved segmentation accuracy of high-resolution remote sensing (RS) images. However, the interpretation of RS images from single sensor is still challenging due to the variety and complexity of land objects, the extreme imbalance distributions of land objects on size and numbers. In contrast, multiple sensors can provide complementary information on the land classes, and thus benefit the interpretation. In this context, this research explores the joint use of RGB optical bands and normalized DSM (nDSM) to analyze an urban scene. The method firstly concatenated three channels RGB image and one channel nDSM band into a four-channel image. Thereafter, ResNet-101 network with fine adjustment were utilized as the backbone network to retain multiple feature information by residual blocks. Then the augmented RGB and nDSM images were used to training the network. The established model was evaluated on the Postdam test set. Results show that the proposed method achieves 86.85% on Overall Accuracy (OA), 77.42% Mean Intersection Over Union (MIOU), which is 6.88% and 11.39% higher than the result achieved by single RGB images. Especially, small targets, such as car and tree, are higher. The experimental results show that the simple structure adjustment of ResNet-101 network can achieve good segmentation performance on RS images (especially small targets) after the combination of twice augmented RGB channels and nDSM channels respectively. In addition, with the addition of nDSM, the accuracy of buildings and trees with height information has been improved.

Soil erosion can cause various ecological problems, such as land degradation, soil fertility loss, and river siltation. Rainfall is the primary water-driven force for soil erosion, and its potential effect on soil erosion is reflected by rainfall erosivity that relates to the raindrop kinetic energy. As it is difficult to observe large-scale dynamic characteristics of raindrops, all the current rainfall erosivity models use the function based on rainfall amount to represent the raindrops' kinetic energy. With the development of global atmospheric re-analysis data, numerical weather prediction techniques become a promising way to estimate rainfall kinetic energy directly at regional and global scales with high spatial and temporal resolutions. This study proposed a novel method for large-scale and long-term rainfall erosivity investigations based on the Weather Research and Forecasting (WRF) model, avoiding errors caused by inappropriate rainfall–energy relationships and large-scale interpolation. We adopted three microphysical parameterizations schemes (Morrison, WDM6, and Thompson aerosol-aware) to obtain raindrop size distributions, rainfall kinetic energy, and rainfall erosivity, with validation by two disdrometers and 304 rain gauges around the United Kingdom. Among the three WRF schemes, Thompson aerosol-aware had the best performance compared with the disdrometers at a monthly scale. The results revealed that high rainfall erosivity occurred in the west coast area at the whole country scale during 2013–2017. The proposed methodology makes a significant contribution to improving large-scale soil erosion estimation and for better understanding microphysical rainfall–soil interactions to support the rational formulation of soil and water conservation planning.

Soil moisture plays an important role in the partitioning of rainfall into evapotranspiration, infiltration, and runoff, hence a vital state variable in hydrological modelling. However, due to the heterogeneity of soil moisture in space, most existing in situ observation networks rarely provide sufficient coverage to capture the catchment-scale soil moisture variations. Clearly, there is a need to develop a systematic approach for soil moisture network design, so that with the minimal number of sensors the catchment spatial soil moisture information could be captured accurately. In this study, a simple and low-data requirement method is proposed. It is based on principal component analysis (PCA) for the investigation of the network redundancy degree and K-means cluster analysis (CA) and a selection of statistical criteria for the determination of the optimal sensor number and placements. Furthermore, the long-term (10-year) 5 km surface soil moisture datasets estimated through the advanced Weather Research and Forecasting (WRF) model are used as the network design inputs. In the case of the Emilia-Romagna catchment, the results show the proposed network is very efficient in estimating the catchment-scale surface soil moisture (i.e. with NSE and r at 0.995 and 0.999, respectively, for the areal mean estimation; and 0.973 and 0.990, respectively, for the areal standard deviation estimation). To retain 90 % variance, a total of 50 sensors in a 22 124 km2 catchment is needed, and in comparison with the original number of WRF grids (828 grids), the designed network requires significantly fewer sensors. However, refinements and investigations are needed to further improve the design scheme, which are also discussed in the paper.

This study assesses the usability of Weather Research and Forecasting (WRF) model simulated soil moisture for landslide monitoring in the Emilia Romagna region, northern Italy, during the 10-year period between 2006 and 2015. In particular, three advanced land surface model (LSM) schemes (i.e. Noah, Noah-MP, and CLM4) integrated with the WRF are used to provide detailed multi-layer soil moisture information. Through the temporal evaluation with the single-point in situ soil moisture observations, Noah-MP is the only scheme that is able to simulate the large soil drying phenomenon close to the observations during the dry season, and it also has the highest correlation coefficient and the lowest RMSE at most soil layers. It is also demonstrated that a single soil moisture sensor located in a plain area has a high correlation with a significant proportion of the study area (even in the mountainous region 141 km away, based on the WRF-simulated spatial soil moisture information). The evaluation of the WRF rainfall estimation shows there is no distinct difference among the three LSMs, and their performances are in line with a published study for the central USA. Each simulated soil moisture product from the three LSM schemes is then used to build a landslide prediction model, and within each model, 17 different exceedance probability levels from 1 % to 50 % are adopted to determine the optimal threshold scenario (in total there are 612 scenarios). Slope degree information is also used to separate the study region into different groups. The threshold evaluation performance is based on the landslide forecasting accuracy using 45 selected rainfall events between 2014 and 2015. Contingency tables, statistical indicators, and receiver operating characteristic analysis for different threshold scenarios are explored. The results have shown that, for landslide monitoring, Noah-MP at the surface soil layer with 30 % exceedance probability provides the best landslide monitoring performance, with its hit rate at 0.769 and its false alarm rate at 0.289.

Urban flooding exposure is generally investigated with the assumption of stationary disasters and disaster-hit bodies during an event, and thus it cannot satisfy the increasingly elaborate modeling and management of urban floods. In this study, a comprehensive method was proposed to simulate dynamic exposure to urban flooding considering residents' travel behavior. First, a flood simulation was conducted using the LISFLOOD-FP model to predict the spatiotemporal distribution of flooding. Second, an agent-based model was used to simulate residents' movements during the urban flooding period. Finally, to study the evolution and patterns of urban flooding exposure, the exposure of population, roads, and buildings to urban flooding was simulated using Lishui, China, as a case study. The results showed that water depth was the major factor affecting total urban exposure in Lishui. Urban exposure to fluvial flooding was concentrated along the river, while exposure to pluvial flooding was dispersed throughout the area (independent from the river). Additionally, the population distribution on weekends was more variable than on weekdays and was more sensitive to floods. In addition, residents' response behavior (based on their subjective consciousness) may result in increased overall exposure. This study presents the first fully formulated method for dynamic urban flood exposure simulation at a high spatiotemporal resolution. The quantitative results of this study can provide fundamental information for urban flood disaster vulnerability assessment, socioeconomic loss assessment, urban disaster risk management, and emergency response plan establishment.

Submicron aerosol was measured to the southwest of Houston, Texas, during winter and summer 2014 to investigate its seasonal variability. Data from a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) indicated that organic aerosol (OA) was the largest component of nonrefractory submicron particulate matter (NR-PM1) (on average, 38 % ± 13 % and 47 % ± 18 % of the NR-PM1 mass loading in winter and summer, respectively). Positive matrix factorization (PMF) analysis of the OA mass spectra demonstrated that two classes of oxygenated OA (less- and more-oxidized OOA, LO and MO) together dominated OA mass in summer (77 %) and accounted for 39 % of OA mass in winter. The fraction of LO-OOA (out of total OOA) is higher in summer (70 %) than in winter (44 %). Secondary aerosols (sulfate + nitrate + ammonium + OOA) accounted for ∼76 % and 88 % of NR-PM1 mass in winter and summer, respectively, indicating NR-PM1 mass was driven mostly by secondary aerosol formation regardless of the season. The mass loadings and diurnal patterns of these secondary aerosols show a clear winter–summer contrast. Organic nitrate (ON) concentrations were estimated using the NOx+ ratio method, with contributions of 31 %–66 % and 9 %–17 % to OA during winter and summer, respectively. The estimated ON in summer strongly correlated with LO-OOA (r=0.73) and was enhanced at nighttime. The relative importance of aqueous-phase chemistry and photochemistry in processing OOA was investigated by examining the relationship of aerosol liquid water content (LWC) and the sum of ozone (O3) and nitrogen dioxide (NO2) (Ox = O3+NO2) with LO-OOA and MO-OOA. The processing mechanism of LO-OOA apparently was related to relative humidity (RH). In periods of RH < 80 %, aqueous-phase chemistry likely played an important role in the formation of wintertime LO-OOA, whereas photochemistry promoted the formation of summertime LO-OOA. For periods of high RH > 80 %, these effects were opposite those of low-RH periods. Both photochemistry and aqueous-phase processing appear to facilitate increases in MO-OOA concentration except during periods of high LWC, which is likely a result of wet removal during periods of light rain or a negative impact on its formation rate. The nighttime increases in MO-OOA during winter and summer were 0.013 and 0.01 µg MO-OOA per µg of LWC, respectively. The increase in LO-OOA was larger than that for MO-OOA, with increase rates of 0.033 and 0.055 µg LO-OOA per µg of LWC at night during winter and summer, respectively. On average, the mass concentration of LO-OOA in summer was elevated by nearly 1.2 µg m−3 for a ∼20 µg change in LWC, which was accompanied by a 40 ppb change in Ox.

Based on published literature and typical profiles from the Nankai University source library, a total of 3326 chemical profiles of the main primary sources of ambient particulate matter (PM) across China from 1987 to 2017 are investigated and reviewed to trace the evolution of their main components and identify the main influencing factors concerning their evolution. In general, the source chemical profiles are varied with respect to their sources and are influenced by different sampling methods. The most complicated profiles are likely attributed to coal combustion (CC) and industrial emissions (IE). The profiles of vehicle emissions (VE) are dominated by organic carbon (OC) and elemental carbon (EC), and vary due to the changing standards of sulfur and additives in gasoline and diesel as well as the sampling methods used. In addition to the sampling methods used, the profiles of biomass burning (BB) and cooking emissions (CE) are also impacted by the different biofuel categories and cooking types, respectively. The variations of the chemical profiles of different sources, and the homogeneity of the subtype source profiles within the same source category are examined using uncertainty analysis and cluster analysis. As a result, a relatively large variation is found in the source profiles of CC, VE, IE, and BB, indicating that these sources urgently require the establishment of local profiles due to their high uncertainties. The results presented highlight the need for further investigation of more specific markers (e.g., isotopes, organic compounds, and gaseous precursors), in addition to routinely measured components, in order to properly discriminate sources. Although the chemical profiles of the main sources have been previously reported in the literature, it should be noted that some of these chemical profiles are currently out of date and need to be updated immediately. Additionally, in the future, specific focus should be placed on the source profile subtypes, especially with respect to local IE in China.

The air quality of the Texas Gulf Coast region historically has been influenced heavily by regional shipping emissions. However, the effects of the recently established North American Emissions Control Area on aerosol concentrations and properties in this region are presently unknown. In order to better understand the current sources and processing mechanisms influencing coastal aerosol near Houston, a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) was deployed for 3 weeks at a coastal location during May–June 2016. Total mass loadings of organic and inorganic non-refractory aerosol components during onshore flow periods were similar to those published before establishment of the regulations. Based on estimated methanesulfonic acid (MSA) mass loadings and published biogenic MSA / non-sea-salt sulfate (nss-SO4) ratios, an average of over 75 % of the observed nss-SO4 was from anthropogenic sources, predominantly shipping emissions. Mass spectral analysis indicated that for periods with similar backward-trajectory-averaged meteorological conditions, air masses influenced by shipping emissions had an increased mass fraction of ions related to carboxylic acids and larger oxygen-to-carbon ratios than those that avoided shipping lanes, suggesting that shipping emissions increase marine organic aerosol (OA) oxidation state. Amine fragment mass loadings were correlated positively with anthropogenic nss-SO4 during onshore flow, implying anthropogenic–biogenic interaction in marine OA production. Model calculations also suggest that advection of shipping-derived aerosol may enhance inland aqueous-phase secondary OA production. These results imply a continuing role of shipping emissions on aerosol properties over the Gulf of Mexico and suggest that further regulation of shipping fuel sulfur content will reduce coastal submicron aerosol mass loadings near Houston.

To evaluate the environmental effectiveness of the control measures for atmospheric pollution in Shijiazhuang, China, a large-scale controlling experiment for emission sources of atmospheric pollutants (i.e. a temporary emission control action, TECA) was designed and implemented during 1 November 2016 to 9 January 2017. Compared to the no-control action and heating period (NCAHP), under unfavourable meteorological conditions, the mean concentrations of PM2.5, PM10, SO2, NO2, and chemical species (Si, Al, Ca2+, Mg2+) in PM2.5 during the control action and heating period (CAHP) still decreased by 8, 8, 5, 19, 30.3, 4.5, 47.0, and 45.2 %, respectively, indicating that the control measures for atmospheric pollution were effective. The effects of control measures in suburbs were better than those in urban area, especially for the control effects of particulate matter sources. The control effects for emission sources of carbon monoxide (CO) were not apparent during the TECA period, especially in suburbs, likely due to the increasing usage of domestic coal in suburbs along with the temperature decreasing.The results of positive matrix factorization (PMF) analysis showed that crustal dust, secondary sources, vehicle emissions, coal combustion and industrial emissions were main PM2.5 sources. Compared to the whole year (WY) and the no-control action and no-heating period (NCANHP), the contribution concentrations and proportions of coal combustion to PM2.5 increased significantly during other stages of the TECA period. The contribution concentrations and proportions of crustal dust and vehicle emissions to PM2.5 decreased noticeably during the CAHP compared to other stages of the TECA period. The contribution concentrations and proportions of industrial emissions to PM2.5 during the CAHP decreased noticeably compared to the NCAHP. The pollutants' emission sources during the CAHP were in effective control, especially for crustal dust and vehicles. However, the necessary coal heating for the cold winter and the unfavourable meteorological conditions had an offset effect on the control measures for emission sources to some degree. The results also illustrated that the discharge of pollutants might still be enormous even under such strict control measures.The backward trajectory and potential source contribution function (PSCF) analysis in the light of atmospheric pollutants suggested that the potential source areas mainly involved the surrounding regions of Shijiazhuang, i.e. south of Hebei and north of Henan and Shanxi. The regional nature of the atmospheric pollution in the North China Plain revealed that there is an urgent need for making cross-boundary control policies in addition to local control measures given the high background level of pollutants.The TECA is an important practical exercise but it cannot be advocated for as the normalized control measures for atmospheric pollution in China. The direct cause of atmospheric pollution in China is the emission of pollutants exceeding the air environment's self-purification capacity, which is caused by an unreasonable and unhealthy pattern for economic development in China.

The GaoFen-2 satellite (GF-2) is a self-developed civil optical remote sensing satellite of China, which is also the first satellite with the resolution of being superior to 1 meter in China. In this paper, we propose a pan-sharpening method based on guided image filtering, apply it to the GF-2 images and compare the performance to state-of-the-art methods. Firstly, a simulated low-resolution panchromatic band is yielded; thereafter, the resampled multispectral image is taken as the guidance image to filter the simulated low resolution panchromatic Pan image, and extracting the spatial information from the original Pan image; finally, the pan-sharpened result is synthesized by injecting the spatial details into each band of the resampled MS image according to proper weights. Three groups of GF-2 images acquired from water body, urban and cropland areas have been selected for assessments. Four evaluation metrics are employed for quantitative assessment. The experimental results show that, for GF-2 imagery acquired over different scenes, the proposed method can not only achieve high spectral fidelity, but also enhance the spatial details

The influence on soil erosion by different bedrock bareness ratios, different rainfall intensities, different underground pore fissure degrees and rainfall duration are researched through manual simulation of microrelief characteristics of karst bare slopes and underground karst crack construction in combination with artificial simulation of rainfall experiment. The results show that firstly, when the rainfall intensity is small (30 and 50 mm h−1), no bottom load loss is produced on the surface, and surface runoff, underground runoff and sediment production are increased with the increasing of rainfall intensity. Secondly, surface runoff and sediment production reduced with increased underground pore fissure degree, while underground runoff and sediment production increased. Thirdly, raindrops hit the surface, forming a crust with rainfall duration. The formation of crusts increases surface runoff erosion and reduces soil infiltration rate. This formation also increases surface-runoff-erosion-damaged crust and increased soil seepage rate. Raindrops continued to hit the surface, leading the formation of crust. Soil permeability showed volatility which was from reduction to increases, reduction, and so on. Surface and subsurface runoff were volatile with rainfall duration. Fourthly, when rock bareness ratio is 50 % and rainfall intensities are 30 and 50 mm h−1, runoff is not produced on the surface, and the slope runoff and sediment production present a fluctuating change with increased rock bareness ratio. Fifthly, the correlation degree between the slope runoff and sediment production and all factors are as follows: rainfall intensity-rainfall duration-underground pore fissure degree–bedrock bareness ratio.