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Ultraviolet (UV) radiation is closely related to health; however, limited measurements have hindered further investigation of its health effects in China. Machine learning algorithms have been widely used to predict environmental factors with high accuracy, but a limited number of studies have implemented it for UV radiation. The main aim of this study is to develop a UV radiation prediction model using the random forest approach and predict the UV radiation with a daily and 10 km resolution in mainland China from 2005 to 2020. The model was developed with multiple predictors, such as UV radiation data from satellites as independent variables and ground UV radiation measurements from monitoring stations as the dependent variable. Missing satellite-based UV radiation data were obtained using the 3 d moving average method. The model performance was evaluated using multiple cross-validation (CV) methods. The overall R2 and root mean square error between measured and predicted UV radiation from model development and model 10-fold CV were 0.97 and 15.64 W m−2 and 0.83 and 37.44 W m−2 at the daily level, respectively. The model that incorporated erythemal daily dose (EDD) retrieved from the Ozone Monitoring Instrument (OMI) had a higher prediction accuracy than that without it. Based on predictions of UV radiation at the daily level, 10 km spatial resolution, and nearly 100 % spatiotemporal coverage, we found that UV radiation increased by 4.20 %, PM2.5 levels decreased by 48.51 %, and O3 levels increased by 22.70 % from 2013–2020, suggesting a potential correlation among these environmental factors. The uneven spatial distribution of UV radiation was associated with factors such as latitude, elevation, meteorological factors, and season. The eastern areas of China pose a higher risk due to both high population density and high UV radiation intensity. Using a machine learning algorithm, this study generated a gridded UV radiation dataset with extensive spatiotemporal coverage, which can be utilized for future health-related research. This dataset is freely available at https://doi.org/10.5281/zenodo.10884591 (Jiang et al., 2024).

The South China Block is crucial for the global study of Cambrian to Ordovician conodont biostratigraphy, but research on its northeastern margin is relatively scarce. Chuzhou, located at the intersection of the South China Block, North China Block, and the Qinling–Dabie orogenic belt, boasts a significant thickness (>1100 m) of carbonate-dominated sedimentary succession during the Cambrian Furongian to Middle Ordovician period. In this study, detailed field measurements and sample collection were conducted on two well-exposed sections in the Chuzhou area. Nine conodont biozones and three assemblages were identified from the Furongian Stage 10 to the Middle Ordovician Darriwilian, including the Cordylodus proavus, Cordylodus lindstromi, Iapetoganathus fluctivagus, Cordylodus angulatus, Rossodus manitouensis, Colaptoconus quadraplicatus, Paltodus deltifer, Paroistodus proteus, and Triangulodus bifidus zones, as well as the Juanognathus anhuiensis–Protopanderodus gradates, Nasusgnathus giganteus–Paroistodus parallelus, and Dapsilodus virtualis–Protopanderodus robustus assemblages. The analysis of conodont biogeographic zonation indicates that conodonts in the study area were predominantly from the North American Midcontinent Province (warm-water biota) and North Atlantic Province (cold-water biota) from Stage 10 to the middle Tremadocian and from the late Tremadocian to the Darriwilian, respectively. This conodont biogeographic transition is comparable to that in the Tarim, North China, and Qinling blocks but precedes that in the interior of the South China Block, likely associated with the Early–Middle Ordovician global paleogeographic and surface oceanic models that led to the changeover of ocean currents in the study area from warm to cold. Furthermore, the Iapetognathus fluctivagus zone, defined as the Ordovician lowest boundary, was first discovered in the Lower Yangtze region and revealed the distribution of this species in the South China Block. The discovery of Parapanderodus lanceolatus in the early Tremadocian suggests a possible exchange of seawater between the Yangtze and Tarim blocks.

Wetlands and reservoirs are important water flow and storage regulators in a river basin; therefore, they can play a crucial role in mitigating flood and hydrological drought risks. Despite the advancement of river basin theory and modeling, our knowledge is still limited about the extent to which these two regulators could perform such a role, especially under future climate extremes. To improve our understanding, we first coupled wetlands and reservoir operations into a semi-spatially explicit hydrological model and then applied it in a case study involving a large river basin in northeast China. The projection of future floods and hydrological droughts was performed using the hydrological model during different periods (near future: 2026–2050, middle century: 2051–2075, and end century: 2076–2100) under five future climate change scenarios. We found that the risk of future floods and hydrological droughts can vary across different periods – in particular, it will experience relatively large increases and slight decreases. This large river basin will experience flood events of longer duration, with larger peak flows and volume, and of enhanced flashiness compared to the historical period. Simultaneously, the hydrological droughts will be much more frequent, with longer durations and more serious deficits. Therefore, the risk of floods and droughts will, overall, increase further under future climate change even under the combined influence of reservoirs and wetlands. These findings highlight the hydrological regulation function of wetlands and reservoirs and attest that the combining of wetlands with reservoir operation cannot fully eliminate the increasing future flood and drought risks. To improve a river basin's resilience to the risks of future climate change, we argue that the implementation of wetland restoration and the development of accurate forecasting systems for effective reservoir operation are of great importance. Furthermore, this study demonstrated a wetland–reservoir integrated modeling and assessment framework that is conducive to risk assessment of floods and hydrological droughts and that can be used for other river basins in the world.

Neural networks were explored to achieve a binary classification for determining land corresponding to peat for a study area in the boreal forest of northern Ontario, Canada. Environmental covariates were employed as predictors and obtained from multiple sources, which included multispectral imagery, LiDAR, SAR, and aeromagnetic data. A dense neural network (DNN), as well as a convolutional neural network (CNN), were each implemented. Logistic regression, support vector machine (SVM) and random forest (RF) approaches were also modelled. Neighboring pixels surrounding the soil sampling sites were incorporated as input into the CNN, that permitted training on additional information that was not exploited by other methods. Preliminary results indicate that a CNN can attain improved accuracies for peat classification, when compared against other approaches.

This study aimed to exploit the use of deep learning networks in the retrieval of the biophysical and biochemical parameters of vegetation canopies. Convolutional Neural Network (CNN), network with only fully connected layers, referred as dense network (DNN), and Autoencoder (AE) were investigated to retrieve leaf area index (LAI) and leaf chlorophyll content. Hyperspectral data simulated by the coupled PROSPECT and SAIL model were used for training and validation. The real CASI hyperspectral data in 50 spectral channels ranging from 522.4 nm to 894.2 nm collected over three agricultural crop fields during the growing season of 2001 were used, together with the in-situ measured LAI and leaf chlorophyll content, as independent test set. Occlusion analysis was also employed to determine the important spectral bands at which reflectance made more contributions to the retrieval with a CNN and interpret the latent variables of the AE. Satisfactory results from these deep learning networks were obtained, compared with ground truth. The DNN with the input of the vegetation indices sensitive to LAI and leaf chlorophyll content (MTVI2 and TCARI/OSAVI) generated the best results with R2 of 0.86 for LAI and 0.55 for leaf chlorophyll content.

Because nitrous acid (HONO) photolysis is a key source of hydroxyl (OH) radicals, identifying the atmospheric sources of HONO is essential to enhance the understanding of atmospheric chemistry processes and improve the accuracy of simulation models. We performed seasonal field observations of HONO in a coastal city of southeastern China, along with measurements of trace gases, aerosol compositions, photolysis rate constants (J), and meteorological parameters. The results showed that the average observed concentration of HONO was 0.54 ± 0.47 ppb. Vehicle exhaust emissions contributed an average of 1.45 % to HONO, higher than the values found in most other studies, suggesting an influence from diesel vehicle emissions. The mean conversion frequency of NO2 to HONO in the nighttime was the highest in summer due to water droplets evaporating under high-temperature conditions. Based on a budget analysis, the rate of emission from unknown sources (Runknown) was highest around midday, with values of 4.51 ppb h−1 in summer, 3.51 ppb h−1 in spring, 3.28 ppb h−1 in autumn, and 2.08 ppb h−1 in winter. Unknown sources made up the largest proportion of all sources in summer (81.25 %), autumn (73.99 %), spring (70.87 %), and winter (59.28 %). The photolysis of particulate nitrate was probably a source in spring and summer while the conversion from NO2 to HONO on BC enhanced by light was perhaps a source in autumn and winter. The variation of HONO at night can be exactly simulated based on the HONO / NOx ratio, while the J(NO3-_R) × pNO3- should be considered for daytime simulations in summer and autumn, or 1/4× (J(NO3-_R) × pNO3-) in spring and winter. Compared with O3 photolysis, HONO photolysis has long been an important source of OH except for summer afternoons. Observation of HONO across four seasons with various auxiliary parameters improves the comprehension of HONO chemistry in southeastern coastal China.

Precipitation data with high resolution and high accuracy are significantly important in numerous hydrological applications. To enhance the spatial resolution and accuracy of satellite-based precipitation products, an easy-to-use downscaling-calibration method based on a spatial random forest (SRF-DC) is proposed in this study, where the spatial autocorrelation of precipitation measurements between neighboring locations is considered. SRF-DC consists of two main stages. First, the satellite-based precipitation is downscaled by the SRF with the incorporation of high-resolution variables including latitude, longitude, normalized difference vegetation index (NDVI), digital elevation model (DEM), terrain slope, aspect, relief and land surface temperatures. Then, the downscaled precipitation is calibrated by the SRF with rain gauge observations and the aforementioned high-resolution variables. The monthly Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) over Sichuan Province, China, from 2015 to 2019 was processed using SRF-DC, and its results were compared with those of classical methods including geographically weighted regression (GWR), artificial neural network (ANN), random forest (RF), kriging interpolation only on gauge measurements, bilinear interpolation-based downscaling and then SRF-based calibration (Bi-SRF), and SRF-based downscaling and then geographical difference analysis (GDA)-based calibration (SRF-GDA). Comparative analyses with respect to root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (CC) demonstrate that (1) SRF-DC outperforms the classical methods as well as the original IMERG; (2) the monthly based SRF estimation is slightly more accurate than the annually based SRF fraction disaggregation method; (3) SRF-based downscaling and calibration perform better than bilinear downscaling (Bi-SRF) and GDA-based calibration (SRF-GDA); (4) kriging is more accurate than GWR and ANN, whereas its precipitation map loses detailed spatial precipitation patterns; and (5) based on the variable-importance rank of the RF, the precipitation interpolated by kriging on the rain gauge measurements is the most important variable, indicating the significance of incorporating spatial autocorrelation for precipitation estimation.

The objective of this study was to explore the utilization of deep learning networks in individual tree crown (ITC) delineation, a very important step in individual tree analysis. Even though many traditional machine learning methods have been developed for ITC delineation, the accuracy remains low, especially for dense forests where branches, crowns, and clusters of trees usually have similar characteristics and boundaries of tree crowns are not distinct. Advance in deep learning provides a good opportunity to improve ITC delineation. In this study, U-net, Residual U-net, and attention U-net were implemented for the first time in ITC delineation. In order to ensure that the boundaries of tree crowns were classified correctly, a weight map was generated to give more weights to boundary pixels between two close crowns in the loss function. These three networks were trained and tested using optical imagery obtained over a study site within the Great Lakes-St. Lawrence forest region, Ontario Canada. Based on two test sites dominated by open mixed forest and closed deciduous forests, respectively, the overall accuracies were 0.94 and 0.90, respectively for U-net, 0.89 and 0.62 for Residual U-net, and 0.96 and 0.83 for attention U-net.

Analytical Hierarchy Process (AHP) with fuzzy logic inference on attributes was employed to determine areas most suitable for agriculture in the Gordon Cosens Forest (GCF) region within the District of Cochrane in northern Ontario, Canada. Attribute layers considered were soil texture, ELC (Ecological Land Classification) moisture regime, slope, canopy height model (CHM), distance to existing road networks and distance to water bodies. Fuzzy logic inference was utilized to rescale the attributes to a normalized range, taking into account preferability, which was then subjected to pairwise comparisons via AHP to determine the attribute layers' weightings. For the study area, the localities identified as most compatible for agricultural development include the southeastern section of the GCF at approximately 30 km south of the community of Fauquier and the westernmost area of the GCF at about 10 km east of Mattice.

Longer growing seasons in Northern Ontario are predicted, as a result of climate change. There is the expectation that there will be increased land use conversion from natural forests in Northern Ontario to capitalise on the new economic opportunities resulting from longer growing seasons. This study examines the impacts the land conversion – from forest to agricultural environment – has on the greenhouse gas emissions and soil properties. We use remote sensing technologies for detecting these changes. This paper highlights an automatic method we developed for change detection. The method was applied to the satellite data over a predominantly vegetated area of Northern Ontario for the period 2001 to 2016. The study showed how the forest air and soil properties transform over time from various land disturbances, and how subsequent management schemes affect the environmental properties such as greenhouse gas emissions and the soil carbon stock.

Abstract The estimation of the difference between the new competitive advantages of China's export and the world’s trading powers have been the key measurement problems in China-related studies. In this work, a comprehensive evaluation index system for new export competitive advantages is developed, a soft-sensing model for China’s new export competitive advantages based on the fuzzy entropy weight analytic hierarchy process is established, and the soft-sensing values of key indexes are derived. The obtained evaluation values of the main measurement index are used as the input variable of the fuzzy least squares support vector machine, and a soft-sensing model of the key index parameters of the new export competitive advantages of China based on the combined soft-sensing model of the fuzzy least squares support vector machine is established. The soft-sensing results of the new export competitive advantage index of China show that the soft measurement model developed herein is of high precision compared with other models, and the technical and brand competitiveness indicators of export products have more significant contributions to the new competitive advantages of China's export, while the service competitiveness indicator of export products has the least contribution to new competitive advantages of China's export.

ABSTRACT: Calcification of bones is the critical process of bone development in birds, which is very important for sustaining the normal biological function of bones. Light is one of the vital factors affecting bone development, but whether light intensity affects bone calcification and the underlying mechanism is still unknown. In this study, we used duck sternum as a model to analyze the calcification process under different light regimes. In addition, the underlying mechanism was also illustrated by integrating metabolomics and transcriptome methods.The experiment lasted from 14 to 51 d of duck age. The control group (LP1) kept light intensity 2 lx during the whole experiment. The two light supplement groups (LP2, LP3) were given light with the intensity of 70 lx at different time (14–29 d for LP2, 14–43 d for LP3). Samples were collected at 52 d of duck age. Sternal calcification analysis showed no significant difference in proportion of area of cartilage matrix and trabecular bone in keel tissue among the 3 groups, but the degree of keel calcification in LP3 was higher than in the other 2 groups. Serum metabolomics showed 32 and 28 differentially accumulated metabolites (DAMs) in the 2 comparison groups, LP1 vs. LP3 and LP1 vs. LP2, respectively. Carboxylic acids and derivatives were the most abundant among the DAMs. Sternal transcriptome analysis showed 231 differentially expressed genes (DEGs), including 177 upregulated genes and 54 downregulated genes in group LP1 vs. LP3, and 22 DEGs in group LP1 vs. LP2. Protein-protein interaction (PPI) network analysis on DEGs between LP1 and LP3 showed that genes BTRC, GLI1, BMP4, and FOS were in the core position of the interaction network, and are also involved in bone development. KEGG pathway analysis of DAMs and DEGs showed that differences in Hedgehog signaling pathway, MAPK signaling pathway, apoptosis, energy metabolism, and amino acid metabolism following light treatment seem likely to have contributed to the observed difference in calcification of duck sternum.

Secondary aerosols are a major component of PM2.5, yet their formation mechanisms in the ambient atmosphere are still unclear. Based on field measurements in downtown Beijing, we show that the photolysis of nitrous acid (HONO) may promote the formation of organic and nitrate aerosols in winter in Beijing, which is supported by the fact that the mass concentrations of organic and nitrate aerosols linearly increase as a function of HONO consumed from early morning to noon. The increased nitrate content also leads to the formation of ammonium particulate matter through enhancing the neutralization of nitrate and sulfate by ammonia. We further illustrate that during pollution events in winter in Beijing, over 50 % of the ambient HONO may be related to traffic-related emissions, including direct emissions and formation via the reaction between OH and vehicle-emitted NO. Overall, our results indicate that traffic-related HONO may play an important role in the oxidative capacity and in turn contribute to haze formation in winter in Beijing. The mitigation of HONO and NOx emissions from vehicles may be an effective way to reduce the formation of secondary aerosols and severe haze events in winter in Beijing.

Previous studies have emphasized that the decrease in photolysis rate at the surface induced by the light extinction of aerosols could weaken ozone photochemistry and then reduce surface ozone. However, quantitative studies have shown that weakened photochemistry leads to a much greater reduction in the net chemical production of ozone, which does not match the reduction in surface ozone. This suggests that in addition to photochemistry, some other physical processes related to the variation of ozone should also be considered. In this study, the Weather Research and Forecasting with Chemistry (WRF-Chem) model coupled with the ozone source apportionment method was applied to determine the mechanism of ozone reduction induced by aerosols over central East China (CEC). Our results showed that weakened ozone photochemistry led to a significant reduction in ozone net chemical production, which occurred not only at the surface but also within the lowest several hundred meters in the planetary boundary layer (PBL). Meanwhile, a larger ozone gradient was formed in the vertical direction, which led to the high concentrations of ozone aloft being entrained by turbulence from the top of the PBL to the surface and partly counteracting the reduction in surface ozone. In addition, contribution from dry deposition was weakened due to the decrease in surface ozone concentration. The reduction in the ozone's sink also slowed down the rate of the decrease in surface ozone. Ozone in the upper layer of the PBL was also reduced, which was induced by much ozone aloft being entrained downward. Therefore, by affecting the photolysis rate, the impact of aerosols was a reduction in ozone not only at the surface but also throughout the entire PBL during the daytime over CEC in this study. The ozone source apportionment results showed that 41.4 %–66.3 % of the reduction in surface ozone came from local and adjacent source regions, which suggested that the impact of aerosols on ozone from local and adjacent regions was more significant than that from long-distance regions. The results also suggested that while controlling the concentration of aerosols, simultaneously controlling ozone precursors from local and adjacent source regions is an effective way to suppress the increase in surface ozone over CEC at present.

Bulk density and soil carbon models were fitted for soil samples collected during field campaigns in 2018 and 2019 for the Kapuskasing region of the District of Cochrane in Ontario, Canada. Prediction maps for bulk density and soil carbon were generated for the 0–15 cm depth mineral soil layer. The application of multi-source remotely sensed data as environmental covariates for model predictors was implemented. Environmental covariates were obtained from multispectral satellite imagery, LiDAR (light detection and ranging) retrievals and airborne geomagnetic surveys, as well from a digital elevation model (DEM) for topographic covariates. Two covariates derived from LiDAR, canopy height model (CHM) and gap fraction, were of high variable importance when fitting models for average bulk density; gap fraction had the highest to second highest variable importance for average bulk density when considered among a full set of 76, or reduced sets of 12 or 5 separate predictors respectively. Environmental covariates corresponding to vegetation cover, specifically reflectance from multispectral imagery or LiDAR data, had the highest variable importance when compared with other categories of soil formation factors. Random forest (RF) models were generated, with RF models based upon just 12 predictors obtaining reasonable results with coefficients of determinations (R2) greater than 0.7 for the standard derivation of bulk density, standard deviation of total carbon and average total carbon for the 0–15 cm depth layer.

The objective of this study was to explore the use of multi-source remotely sensed data for individual tree species. To achieve this, a neutrosophic logic-based method was developed for tree species classification using the combined spectral, textural and structural information derived from WorldView-2 (WV-2) multispectral bands, WV-2 panchromatic band, and LiDAR (Light Detection And Ranging)-derived canopy height model (CHM), respectively. The developed method was tested on the data obtained over the Keele campus, York University, Toronto Canada and the KNN (K Nearest Neighbour) classification method. Twenty-one spectral, three textural and three structural features were used to classify five species (Norway maple, honey locust, Austrian pine, blue spruce, and white spruce). For this study, 522 trees were used for training and 223 for testing. The overall classification accuracy obtained by the proposed method was 0.82. It was significantly improved compared with the KNN (0.73), weighted KNN (0.76), and fuzzy KNN (0.75) methods. In addition, Dempster-Shafer (DS) theory was explored to perform information fusion at the decision level in comparison to that at the feature level. The accuracies obtained by the fusion at the decision level were generally lower than those at the feature level. Even though promising results based on the neutrosophic logic were obtained during this proof-concept stage, studies are underway to perform more tests with a large number of tree crowns and more species and exploit other classification methods, such as support vector machine.

Hydroxyl (OH) radicals, nitrate (NO3) radicals and ozone (O3) play central roles in the troposphere because they control the lifetimes of many trace gases that result from anthropogenic and biogenic origins. To estimate the air chemistry, the atmospheric reactivity and oxidation capacity were comprehensively analyzed based on a parameterization method at a suburban site in Xianghe in the North China Plain from 6 July 2018 to 6 August 2018. The total OH, NO3 and O3 reactivities at the site varied from 9.2 to 69.6, 0.7 to 27.5 and 3.3×10-4 to 1.8×10-2 s−1 with campaign-averaged values of 27.5±9.7, 2.2±2.6 and 1.2±1.7×10-3 s−1 (± standard deviation), respectively. NOx (NO+NO2) was by far the main contributor to the reactivities of the three oxidants, with average values of 43 %–99 %. Alkenes dominated the OH, NO3 and O3 reactivities towards total nonmethane volatile organic compounds (NMVOCs), accounting for 42.9 %, 77.8 % and 94.0 %, respectively. The total OH, NO3 and O3 reactivities displayed similar diurnal variations with the lowest values during the afternoon but the highest values during rush hours, and the diurnal profile of NOx appears to be the major driver for the diurnal profiles of the reactivities of the three oxidants. A box model (a model to Simulate the concentrations of Organic vapors, Sulfuric Acid and Aerosols; SOSAA) derived from a column chemical transport model was used to simulate OH and NO3 concentrations during the observation period. The calculated atmospheric oxidation capacity (AOC) reached 4.5×108 moleculescm-3s-1, with a campaign-averaged value of 7.8×107 moleculescm-3s-1 dominated by OH (7.7×107 moleculescm-3s-1, 98.2 %), O3 (1.2×106 moleculescm-3s-1, 1.5 %) and NO3 (1.8×105 moleculescm-3s-1, 0.3 %). Overall, the integration of OH, NO3 and O3 reactivities analysis could provide useful insights for NMVOC pollution control in the North China Plain. We suggest that further studies, especially direct observations of OH and NO3 radical concentrations and their reactivities, are required to better understand trace gas reactivity and AOC.

Under a high atmospheric oxidization capacity, the synergistic effect of the physicochemical processes in the atmospheric boundary layer (ABL) caused summer haze pollution in Beijing. The southern and southwestern areas, generally 60–300 km away from Beijing, were seriously polluted in contrast to Beijing, which remained clean. Southerly winds moving faster than 20–30 km h−1 since the early morning primarily caused haze pollution initiation. The PM2.5 (particulate matter with a dynamic equivalent diameter smaller than 2.5 µm) level increased to 75 µg m−3 over several hours during the daytime, which was simultaneously affected by the ABL structure. Additionally, the O3 concentration was quite high during the daytime (250 µg m−3), corresponding to a high atmospheric oxidation capacity. Much sulfate and nitrate were produced through active atmospheric chemical processes, with sulfur oxidation ratios (SORs) up to ∼0.76 and nitrogen oxidation ratios (NORs) increasing from 0.09 to 0.26, which further facilitated particulate matter (PM) level enhancement. However, the increase in sulfate was mainly linked to southerly transport. At midnight, the PM2.5 concentration sharply increased from 75 to 150 µg m−3 over 4 h and remained at its highest level until the next morning. Under an extremely stable ABL structure, secondary aerosol formation dominated by nitrate was quite intense, driving the haze pollution outbreak. The PM levels in the southern and southeastern areas of Beijing were significantly lower than those in Beijing at this time, even below air quality standards; thus, the contribution of pollution transport had almost disappeared. With the formation of a nocturnal stable boundary layer (NSBL) at an altitude ranging from 0–0.3 km, the extremely low turbulence kinetic energy (TKE) ranging from 0 to 0.05 m2 s−2 inhibited the spread of particles and moisture, ultimately resulting in elevated near-surface PM2.5 and relative humidity (∼90 %) levels. Due to the very high humidity and ambient oxidization capacity, NOR rapidly increased from 0.26 to 0.60, and heterogeneous hydrolysis reactions at the moist particle surface were very notable. The nitrate concentration steeply increased from 11.6 to 57.8 µg m−3, while the sulfate and organics concentrations slightly increased by 6.1 and 3.1 µg m−3, respectively. With clean and strong winds passing through Beijing, the stable ABL dissipated with the potential temperature gradient becoming negative and the ABL height (ABLH) increasing to ∼2.5 km. The high turbulence activity with a TKE ranging from 3 to 5 m2 s−2 notably promoted pollution diffusion. The self-cleaning capacity of the atmosphere is commonly responsible for air pollution dispersion. However, reducing the atmospheric oxidization capacity, through strengthening collaborative control of nitrogen oxide (NOx) and volatile organic compounds (VOCs), as well as continuously deepening regional joint air pollution control, is urgent.

Light and temperature are two essential environmental cues for plants, helping to optimize plant body architecture and physiology. To sense a broad spectrum of sun radiation spanning from UV-B to far-red wavelength, plants are equipped with a sophisticated array of photoreceptors, including phytochromes, cryptochromes, phototropins, Zeitlupes, and UV-B photoreceptor UVR8. On the contrary, since the thermodynamic effects extensively affect the molecular and supramolecular structures, it is difficult to identify the entry point or initial receptor of temperature. Even so, several putative temperature sensors have been proposed, such as calcium ion channels, H2A.Z, and the thermodynamic change of plasma membrane fluidity. Considering that many processes in plant respond to irradiance and temperature, scientists devote to finding out the converge point of these environmental cues. As a typical example, circadian rhythm is such an integration point, which receives the signal input of both irradiance and temperature. The updating evidence shows, as an important photoreceptor, phytochrome B acts as temperature sensors via a thermodynamic active state revision. These findings suggest that the studies on light and temperature receptors in plants should not be separated. Their extensive convergence during signalling provides a new direction for understanding the stimuli perception mechanisms.

Although much effort has been put into studying air pollution, our knowledge of the mechanisms of frequently occurring intense haze episodes in China is still limited. In this study, using 3 years of measurements of air pollutants at three different height levels on a 325 m Beijing meteorology tower, we found that a positive aerosol–boundary layer feedback mechanism existed at three vertical observation heights during intense haze polluted periods within the mixing layer. This feedback was characterized by a higher loading of PM2.5 with a shallower mixing layer. Modelling results indicated that the presence of PM2.5 within the boundary layer led to reduced surface temperature, relative humidity and mixing layer height during an intensive haze episode. Measurements showed that the aerosol–boundary layer feedback was related to the decrease in solar radiation, turbulent kinetic energy and thereby suppression of the mixing layer. The feedback mechanism can explain the rapid formation of intense haze episodes to some extent, and we suggest that the detailed feedback mechanism warrants further investigation from both model simulations and field observations.

As potential substitutes for traditional free-range rearing system, floor-rearing system (FRS) and net-rearing system (NRS) are the current predominant dryland duck rearing systems. However, the influence of these two systems on production performance and duck health is poorly understood. In this study, a 2 × 3 factorial arrangement with two rearing systems (FRS and NRS) and three ages (4w, 8w and 13w) was conducted to study the effects of FRS and NRS on production, antioxidant capacity and immune status of Nonghua ducks. The production performance was mainly affected by the effect of rearing systems at 8w. Body weight, average daily gain, eviscerated weight and semi-eviscerated weight were higher in NRS ducks at 8w, but carcass yield at 8w and 13w was decreased (P 0.05), however, in NRS, slightly better antioxidant capacity was seen at 4w, while glutathione peroxidase (GSH-Px) activity was higher at 13w (P

The characteristics of ozone variations and the impacts of synoptic and local meteorological factors in northern China were quantitatively analyzed during the warm season from 2013 to 2017 based on multi-city in situ ozone and meteorological data as well as meteorological reanalysis. The domain-averaged maximum daily 8 h running average O3 (MDA8 O3) concentration was 122±11 µg m−3, with an increase rate of 7.88 µg m−3 yr−1, and the three most polluted months were closely related to the variations in the synoptic circulation patterns, which occurred in June (149 µg m−3), May (138 µg m−3) and July (132 µg m−3). A total of 26 weather types (merged into five weather categories) were objectively identified using the Lamb–Jenkinson method. The highly polluted weather categories included the S–W–N directions (geostrophic wind direction diverts from south to north), low-pressure-related weather types (LP) and cyclone type, which the study area controlled by a low-pressure center (C), and the corresponding domain-averaged MDA8 O3 concentrations were 122, 126 and 128 µg m−3, respectively. Based on the frequency and intensity changes of the synoptic circulation patterns, 39.2 % of the interannual increase in the domain-averaged O3 from 2013 to 2017 was attributed to synoptic changes, and the intensity of the synoptic circulation patterns was the dominant factor. Using synoptic classification and local meteorological factors, the segmented synoptic-regression approach was established to evaluate and forecast daily ozone variability on an urban scale. The results showed that this method is practical in most cities, and the dominant factors are the maximum temperature, southerly winds, relative humidity on the previous day and on the same day, and total cloud cover. Overall, 41 %–63 % of the day-to-day variability in the MDA8 O3 concentrations was due to local meteorological variations in most cities over northern China, except for two cities: QHD (Qinhuangdao) at 34 % and ZZ (Zhengzhou) at 20 %. Our quantitative exploration of the influence of both synoptic and local meteorological factors on interannual and day-to-day ozone variability will provide a scientific basis for evaluating emission reduction measures that have been implemented by the national and local governments to mitigate air pollution in northern China.

As major chemical components of airborne fine particulate matter (PM2.5), organic carbon (OC) and elemental carbon (EC) have vital impacts on air quality, climate change, and human health. Because OC and EC are closely associated with fuel combustion, it is helpful for the scientific community and policymakers assessing the efficacy of air pollution control measures to study the impact of control measures and regional transport on OC and EC levels. In this study, hourly mass concentrations of OC and EC associated with PM2.5 were semi-continuously measured from March 2013 to February 2018. The results showed that annual mean OC and EC concentrations declined from 14.0 to 7.7 µg m−3 and from 4.0 to 2.6 µg m−3, respectively, from March 2013 to February 2018. In combination with the data of OC and EC in previous studies, an obvious decreasing trend in OC and EC concentrations was found, which was caused by clean energy policies and effective air pollution control measures. However, no obvious change in the ratios of OC and EC to the PM2.5 mass (on average, 0.164 and 0.049, respectively) was recorded, suggesting that inorganic ions still contributed a lot to PM2.5. Based on the seasonal variations in OC and EC, it appeared that higher OC and EC concentrations were still observed in the winter months, with the exception of winter of 2017–2018. Traffic policies executed in Beijing resulted in nighttime peaks of OC and EC, caused by heavy-duty vehicles and heavy-duty diesel vehicles being permitted to operate from 00:00 to 06:00 (China standard time, UTC+8, for all times throughout the paper). In addition, the fact that there was no traffic restriction in weekends led to higher concentrations on weekends compared to weekdays. Significant correlations between OC and EC were observed throughout the study period, suggesting that OC and EC originated from common emission sources, such as exhaust of vehicles and fuel combustion. OC and EC levels increased with enhanced SO2, CO, and NOx concentrations while the O3 and OC levels were enhanced simultaneously when O3 concentrations were higher than 50 µg m−3. Non-parametric wind regression analysis was performed to examine the sources of OC and EC in the Beijing area. It was found that there were distinct hot spots in the northeast wind sector at wind speeds of approximately 0–6 km h−1, as well as diffuse signals in the southwestern wind sectors. Source areas further away from Beijing were assessed by potential source contribution function (PSCF) analysis. A high-potential source area was precisely pinpointed, which was located in the northwestern and southern areas of Beijing in 2017 instead of solely in the southern areas of Beijing in 2013. This work shows that improvement of the air quality in Beijing benefits from strict control measures; however, joint prevention and control of regional air pollution in the regions is needed for further improving the air quality. The results provide a reference for controlling air pollution caused by rapid economic development in developing countries.

Quantifying the transport flux (TF) of atmospheric pollutants plays an important role in understanding the causes of air pollution and in making decisions regarding the prevention and control of regional air pollution. In this study, the mixing layer height (MLH) and wind profile were measured by a ceilometer and Doppler wind radar, respectively, and the characteristics of the atmospheric dilution capability were analyzed using these two datasets. The ventilation coefficient (VC) appears to be the highest in the spring (3940±2110 m2 s−1) and lower in the summer (2953±1322 m2 s−1), autumn (2580±1601 m2 s−1) and winter (2913±3323 m2 s−1). Combined with the backscatter measured by the ceilometer, vertical profiles of the PM2.5 concentration were obtained and the PM2.5 TF in the mixing layer was calculated. The TF was the highest in the spring at 4.33±0.69 mg m−1 s−1 and lower in the summer, autumn and winter, when the TF values were 2.27±0.42, 2.39±0.45 and 2.89±0.49 mg m−1 s−1, respectively. Air pollutants transport mainly occurs between 14:00 and 18:00 LT. The TF was large in the pollution transition period (spring: 5.50±4.83 mg m−1 s−1; summer: 3.94±2.36 mg m−1 s−1; autumn: 3.72±2.86 mg m−1 s−1; winter: 4.45±4.40 mg m−1 s−1) and decreased during the heavy pollution period (spring: 4.69±4.84 mg m−1 s−1; summer: 3.39±1.77 mg m−1 s−1; autumn: 3.01±2.40 mg m−1 s−1; winter: 3.25±2.77 mg m−1 s−1). Our results indicate that the influence of the air pollutants transport in the southern regions should receive more focus in the transition period of pollution, while local emissions should receive more focus in the heavy pollution period.

Water vapor has been proposed to amplify the severe haze pollution in China by enhancing the aerosol–radiation feedback (ARF). Observations have revealed that the near-surface PM2.5 concentrations ([PM2.5]) generally exhibit an increasing trend with relative humidity (RH) in the North China Plain (NCP) during 2015 wintertime, indicating that the aerosol liquid water (ALW) caused by hygroscopic growth could play an important role in the PM2.5 formation and accumulation. Simulations during a persistent and heavy haze pollution episode from 5 December 2015 to 4 January 2016 in the NCP were conducted using the WRF-Chem Model to comprehensively quantify contributions of the ALW effect to near-surface [PM2.5]. The WRF-Chem Model generally performs reasonably well in simulating the temporal variations in RH against measurements in the NCP. The factor separation approach (FSA) was used to evaluate the contribution of the ALW effect on the ARF, photochemistry, and heterogeneous reactions to [PM2.5]. The ALW not only augments particle sizes to enhance aerosol backward scattering but also increases the effective radius to favor aerosol forward scattering. The contribution of the ALW effect on the ARF and photochemistry to near-surface [PM2.5] is not significant, being generally within 1.0 µg m−3 on average in the NCP during the episode. Serving as an excellent substrate for heterogeneous reactions, the ALW substantially enhances the secondary aerosol (SA) formation, with an average contribution of 71 %, 10 %, 26 %, and 48 % to near-surface sulfate, nitrate, ammonium, and secondary organic aerosol concentrations. Nevertheless, the SA enhancement due to the ALW decreases the aerosol optical depth and increases the effective radius to weaken the ARF, reducing near-surface primary aerosols. The contribution of the ALW total effect to near-surface [PM2.5] is 17.5 % on average, which is overwhelmingly dominated by enhanced SA. Model sensitivities also show that when the RH is less than 80 %, the ALW progressively increases near-surface [PM2.5] but commences to decrease when the RH exceeds 80 % due to the high occurrence frequencies of precipitation.

Atmospheric aerosols scatter or absorb a fraction of the incoming solar radiation to cool or warm the atmosphere, decreasing surface temperature and altering atmospheric stability to further affect the dispersion of air pollutants in the planetary boundary layer (PBL). In the present study, simulations during a persistent and heavy haze pollution episode from 5 December 2015 to 4 January 2016 in the North China Plain (NCP) were performed using the Weather Research and Forecasting model with Chemistry (WRF-Chem) to comprehensively quantify contributions of aerosol shortwave radiative feedback (ARF) to near-surface (around 15 m above the ground surface) PM2.5 mass concentrations. The WRF-Chem model generally performs well in simulating the temporal variations and spatial distributions of air pollutants concentrations compared to observations at ambient monitoring sites in the NCP, and the simulated diurnal variations of aerosol species are also consistent with the measurements in Beijing. Additionally, the model simulates the aerosol radiative properties, the downward shortwave flux, and the PBL height against observations in the NCP well. During the episode, ARF deteriorates the haze pollution, increasing the near-surface PM2.5 concentrations in the NCP by 10.2 µg m−3 or with a contribution of 7.8 % on average. Sensitivity studies have revealed that high loadings of PM2.5 attenuate the incoming solar radiation reaching the surface to cool the low-level atmosphere, suppressing the development of the PBL, decreasing the surface wind speed, further hindering the PM2.5 dispersion, and consequently exacerbating the haze pollution in the NCP. Furthermore, when the near-surface PM2.5 mass concentration increases from around 50 to several hundred µg m−3, ARF contributes to the near-surface PM2.5 by more than 20 % during daytime in the NCP, substantially aggravating the heavy haze formation. However, when the near-surface PM2.5 concentration is less than around 50 µg m−3, ARF generally reduces the near-surface PM2.5 concentration due to the consequent perturbation of atmospheric dynamic fields.

The detailed formation mechanism of an increased number of haze events in China is still not very clear. Here, we found that reduced surface visibility from 1980 to 2010 and an increase in satellite-derived columnar concentrations of inorganic precursors from 2002 to 2012 are connected with each other. Typically, higher inorganic mass fractions lead to increased aerosol water uptake and light-scattering ability in elevated relative humidity. Satellite observation of aerosol precursors of NO2 and SO2 showed increased concentrations during the study period. Our in situ measurement of aerosol chemical composition in Beijing also confirmed increased contribution of inorganic aerosol fraction as a function of the increased particle pollution level. Our investigations demonstrate that the increased inorganic fraction in the aerosol particles is a key component in the frequently occurring haze days during the study period, and particularly the reduction of nitrate, sulfate and their precursor gases would contribute towards better visibility in China.

To have a better understanding of how the “gut–liver axis” mediates the lipid deposition in the liver, a comparison of overfeeding influence on intestine physiology and microbiota between Gang Goose and Tianfu Meat Goose was performed in this study. After force-feeding, compared with Gang Goose, Tianfu Meat Goose had better fat storage capacity in liver (397.94 vs. 166.54 for foie gras weight (g), P

The implementation of green mining (GM) can improve the quality of economic growth. Based on uncertainty measurement theory (UMT), we aim to develop a reasonable method of green mining (GM) evaluation for building material (BM) mines via comparative analysis. At first, establish UMT evaluating model for GM. The linear uncertainty (LU) measurement function was employed to solve single indicator measure values. Then, based on the entropy weight method (EWM), the analytic hierarchy process (AHP) and a method that couples AHP and EWM (EWM-AHP), and the weights of the evaluating indicator were determined. At last, based on UMT, a total of 6 coupled methods are developed to evaluate the GM grades. The GM grades of two building material mines in China were evaluated by applying the 6 methods. Future suggestions were given to the enterprises to improve the GM grades. According to the actual situations of GM in the two building material (BM) mines, it can be speculated that UMT-EWM-AHP-CDRT is the most reasonable method. The reasonable method can not only evaluate the GM grades for BM but also provide guidance to the development of GM for BM.

The Campaign on Atmospheric Aerosol Research network of China (CARE-China) is a long-term project for the study of the spatio-temporal distributions of physical aerosol characteristics as well as the chemical components and optical properties of aerosols over China. This study presents the first long-term data sets from this project, including 3 years of observations of online PM2.5 mass concentrations (2012–2014) and 1 year of observations of PM2.5 compositions (2012–2013) from the CARE-China network. The average PM2.5 concentration at 20 urban sites is 73.2 µg m−3 (16.8–126.9 µg m−3), which was 3 times higher than the average value from the 12 background sites (11.2–46.5 µg m−3). The PM2.5 concentrations are generally higher in east-central China than in the other parts of the country due to their relatively large particulate matter (PM) emissions and the unfavourable meteorological conditions for pollution dispersion. A distinct seasonal variability in PM2.5 is observed, with highs in the winter and lows during the summer at urban sites. Inconsistent seasonal trends were observed at the background sites. Bimodal and unimodal diurnal variation patterns were identified at both urban and background sites. The chemical compositions of PM2.5 were analysed at six paired urban and background sites located within the most polluted urban agglomerations – North China Plain (NCP), Yangtze River delta (YRD), Pearl River delta (PRD), North-east China region (NECR), South-west China region (SWCR) – and the cleanest region of China – the Tibetan Autonomous Region (TAR). The major PM2.5 constituents across all the urban sites are organic matter (OM, 26.0 %), SO42− (17.7 %), mineral dust (11.8 %), NO3− (9.8 %), NH4+ (6.6 %), elemental carbon (EC) (6.0 %), Cl− (1.2 %) at 45 % RH and unaccounted matter (20.7 %). Similar chemical compositions of PM2.5 were observed at background sites but were associated with higher fractions of OM (33.2 %) and lower fractions of NO3− (8.6 %) and EC (4.1 %). Significant variations of the chemical species were observed among the sites. At the urban sites, the OM ranged from 12.6 µg m−3 (Lhasa) to 23.3 µg m−3 (Shenyang), the SO42− ranged from 0.8 µg m−3 (Lhasa) to 19.7 µg m−3 (Chongqing), the NO3− ranged from 0.5 µg m−3 (Lhasa) to 11.9 µg m−3 (Shanghai) and the EC ranged from 1.4 µg m−3 (Lhasa) to 7.1 µg m−3 (Guangzhou). The PM2.5 chemical species at the background sites exhibited larger spatial heterogeneities than those at urban sites, suggesting different contributions from regional anthropogenic or natural emissions and from long-range transport to background areas. Notable seasonal variations of PM2.5-polluted days were observed, especially for the megacities in east-central China, resulting in frequent heavy pollution episodes occurring during the winter. The evolution of the PM2.5 chemical compositions on polluted days was consistent for the urban and nearby background sites, where the sum of sulfate, nitrate and ammonia typically constituted much higher fractions (31–57 %) of PM2.5 mass, suggesting fine-particle pollution in the most polluted areas of China assumes a regional tendency, and the importance of addressing the emission reduction of secondary aerosol precursors including SO2 and NOx. Furthermore, distinct differences in the evolution of [NO3−]∕[SO42−] ratio and OC∕EC ratio on polluted days imply that mobile sources and stationary (coal combustion) sources are likely more important in Guangzhou and Shenyang, respectively, whereas in Beijing it is mobile emission and residential sources. As for Chongqing, the higher oxidation capacity than the other three cities suggested it should pay more attention to the emission reduction of secondary aerosol precursors. This analysis reveals the spatial and seasonal variabilities of the urban and background aerosol concentrations on a national scale and provides insights into their sources, processes and lifetimes.

The black duck population has decreased significantly due to loss of its breeding habitat. Wetlands are an important feature that relates to habitat management and requires monitoring. Synthetic Aperture Radar (SAR) systems are helpful to map the wetland as the microwave signals are sensitive to water content and can be used to map surface water extent, saturated soils, and flooded vegetation. In this study, RadarSat 2 Polarimetric data is employed to map surface water and track changes in extent over the years through image thresholding and reviewed different approaches of Polarimetric decompositions for detecting flooded vegetation. Also, object-based analysis associated with beaver activity is conducted with combined multispectral SPOT satellite imagery. Results show SAR data has proven ability to improve mapping open water areas and locate flooded vegetation areas.

Stability analysis is of great significance to landslide hazard prevention, especially the dynamic stability. However, many existing stability analysis methods are difficult to analyse the continuous landslide stability and its changing regularities in a uniform criterion due to the unique landslide geological conditions. Based on the relationship between displacement monitoring data, deformation states and landslide stability, a state fusion entropy method is herein proposed to derive landslide instability through a comprehensive multi-attribute entropy analysis of deformation states, which are defined by a proposed joint clustering method combining K-means and a cloud model. Taking Xintan landslide as the detailed case study, cumulative state fusion entropy presents an obvious increasing trend after the landslide entered accelerative deformation stage and historical maxima match highly with landslide macroscopic deformation behaviours in key time nodes. Reasonable results are also obtained in its application to several other landslides in the Three Gorges Reservoir in China. Combined with field survey, state fusion entropy may serve for assessing landslide stability and judging landslide evolutionary stages.

To investigate the spatiotemporal variability of the mixing layer height (MLH) on the North China Plain (NCP), multi-site and long-term observations of the MLH with ceilometers at three inland stations (Beijing, BJ; Shijiazhuang, SJZ; Tianjin, TJ) and one coastal site (Qinhuangdao) were conducted from 16 October 2013 to 15 July 2015. The MLH of the inland stations in the NCP were highest in summer and lowest in winter, while the MLH on the coastal area of Bohai was lowest in summer and highest in spring. As a typical site in southern Hebei, the annual mean of the MLH at SJZ was 464 ± 183 m, which was 15.0 and 21.9 % lower than that at the BJ (594 ± 183 m) and TJ (546 ± 197 m) stations, respectively. Investigation of the shear term and buoyancy term in the NCP revealed that these two parameters in southern Hebei were 2.8 times lower and 1.5 times higher than that in northern NCP within 0–1200 m in winter, respectively, leading to a 1.9-fold higher frequency of the gradient Richardson number > 1 in southern Hebei compared to the northern NCP. Furthermore, combined with aerosol optical depth and PM2.5 observations, we found that the pollutant column concentration contrast (1.2 times) between these two areas was far less than the near-ground PM2.5 concentration contrast (1.5 times). Through analysis of the ventilation coefficient in the NCP, the near-ground heavy pollution in southern Hebei mainly resulted from the lower MLH and wind speed. Therefore, due to the importance of unfavorable weather conditions, heavily polluting enterprises should be relocated and strong emission reduction measures should be introduced to improve the air quality in southern Hebei.

The objectives of this study were to exploit the synergy of hyperspectral imagery, Light Detection And Ranging (LiDAR) and high spatial resolution data and their synergy in the early detection of the EAB (Emerald Ash Borer) presence in trees within urban areas and to develop a framework to combine information extracted from multiple data sources. To achieve these, an object-oriented framework was developed to combine information derived from available data sets to characterize ash trees. Within this framework, an advanced individual tree delineation method was developed to delineate individual trees using the combined high-spatial resolution worldview-3 imagery was used together with LiDAR data. Individual trees were then classified to ash and non-ash trees using spectral and spatial information. In order to characterize the health state of individual ash trees, leaves from ash trees with various health states were sampled and measured using a field spectrometer. Based on the field measurements, the best indices that sensitive to leaf chlorophyll content were selected. The developed framework and methods were tested using worldview-3, airborne LiDAR data over the Keele campus of York University Toronto Canada. Satisfactory results in terms of individual tree crown delineation, ash tree identification and characterization of the health state of individual ash trees. Quantitative evaluations is being carried out.

The simultaneous observation and analysis of atmospheric fine particles (PM2.5) on a regional scale is an important approach to develop control strategies for haze pollution. In this study, samples of filtered PM2.5 were collected simultaneously at three urban sites (Beijing, Tianjin, and Shijiazhuang) and at a regional background site (Xinglong) in the Beijing–Tianjin–Hebei (BTH) region from June 2014 to April 2015. The PM2.5 at the four sites was mainly comprised of organic matter, secondary inorganic ions, and mineral dust. Positive matrix factorization (PMF) demonstrated that, on an annual basis, secondary inorganic aerosol was the largest PM2.5 source in this region, accounting for 29.2–40.5 % of the PM2.5 mass at the urban sites; the second-largest PM2.5 source was motor vehicle exhaust, particularly in Beijing (24.9 %), whereas coal combustion was also a large source in Tianjin (12.4 %) and Shijiazhuang (15.5 %), with particular dominance in winter. Secondary inorganic aerosol plays a vital role in the haze process, with the exception of the spring haze in Shijiazhuang and Tianjin, for which the dust source was crucial. In addition to secondary transformations, local direct emissions (coal combustion and motor vehicle exhaust) significantly contribute to the winter haze at the urban sites. Moreover, with the aggravation of haze pollution, the OC ∕ EC mass ratio of PM2.5 decreased considerably and the nitrate-rich secondary aerosol increased during all four seasons in Beijing, both of which indicate that local motor vehicle emissions significantly contribute to the severe haze episodes in Beijing. To assess the impacts of regional transport on haze pollution, the PMF results were further processed with backward-trajectory cluster analysis, revealing that haze pollution usually occurred when air masses originating from polluted industrial regions in the south prevailed and is characterized by high PM2.5 loadings with considerable contributions from secondary aerosols. This study suggests that control strategies to mitigate haze pollution in the BTH region should focus on the reduction of gaseous precursor emissions from fossil fuel combustion (motor vehicle emissions in Beijing and coal combustion in Tianjin, Hebei, and nearby provinces).

A novel algorithm for forest road identification and extraction was developed. The algorithm utilized Laplacian of Gaussian (LoG) filter and slope calculation on high resolution multispectral imagery and LiDAR data respectively to extract both primary road and secondary road segments in the forest area. The proposed method used road shape feature to extract the road segments, which have been further processed as objects with orientation preserved. The road network was generated after post processing with tensor voting. The proposed method was tested on Hearst forest, located in central Ontario, Canada. Based on visual examination against manually digitized roads, the majority of roads from the test area have been identified and extracted from the process.

The inability to detect the Emerald Ash Borer (EAB) at an early stage has led to the enumerable loss of different species of ash trees. Due to the increasing risk being posed by the EAB, a robust and accurate method is needed for identifying Individual Tree Crowns (ITCs) that are at a risk of being infected or are already diseased. This paper attempts to outline an ITC delineation method that employs airborne multi-spectral Light Detection and Ranging (LiDAR) to accurately delineate tree crowns. The raw LiDAR data were initially pre-processed to generate the Digital Surface Models (DSM) and Digital Elevation Models (DEM) using an iterative progressive TIN (Triangulated Irregular Network) densification method. The DSM and DEM were consequently used for Canopy Height Model (CHM) generation, from which the structural information pertaining to the size and shape of the tree crowns was obtained. The structural information along with the spectral information was used to segment ITCs using a region growing algorithm. The availability of the multi-spectral LiDAR data allows for delineation of crowns that have otherwise homogenous structural characteristics and hence cannot be isolated from the CHM alone. This study exploits the spectral data to derive initial approximations of individual tree tops and consequently grow those regions based on the spectral constraints of the individual trees.

This study investigates a cross-variable ozone data assimilation (DA) method based on an ensemble Kalman filter (EnKF) that has been used in the companion study to improve ozone forecasts over Beijing and surrounding areas. The main purpose is to delve into the impacts of the cross-variable adjustment of nitrogen oxide (NOx) emissions on the nitrogen dioxide (NO2) forecasts over this region during the 2008 Beijing Olympic Games. A mixed effect on the NO2 forecasts was observed through application of the cross-variable assimilation approach in the real-data assimilation (RDA) experiments. The method improved the NO2 forecasts over almost half of the urban sites with reductions of the root mean square errors (RMSEs) by 15–36 % in contrast to big increases of the RMSEs over other urban stations by 56–239 %. Over the urban stations with negative DA impacts, improvement of the NO2 forecasts (with 7 % reduction of the RMSEs) was noticed at night and in the morning versus significant deterioration during daytime (with 190 % increase of the RMSEs), suggesting that the negative data assimilation impacts mainly occurred during daytime. Ideal-data assimilation (IDA) experiments with a box model and the same cross-variable assimilation method confirmed the mixed effects found in the RDA experiments. In the same way, NOx emission estimation was improved at night and in the morning even under large biases in the prior emission, while it deteriorated during daytime (except for the case of minor errors in the prior emission). The mixed effects observed in the cross-variable data assimilation, i.e., positive data assimilation impacts on NO2 forecasts over some urban sites, negative data assimilation impacts over the other urban sites, and weak data assimilation impacts over suburban sites, highlighted the limitations of the EnKF under strong nonlinear relationships between chemical variables. Under strong nonlinearity between daytime ozone concentrations and NOx emissions uncertainties (with large biases in the a priori emission), the EnKF may come up with inefficient or wrong adjustments to NOx emissions. The present findings reveal that bias correction is essential for the application of the EnKF in dealing with the data assimilation problem over strong nonlinear system.

The mixing layer is an important meteorological factor that affects air pollution. In this study, the atmospheric mixing layer height (MLH) was observed in Beijing from July 2009 to December 2012 using a ceilometer. By comparison with radiosonde data, we found that the ceilometer underestimates the MLH under conditions of neutral stratification caused by strong winds, whereas it overestimates the MLH when sand-dust is crossing. Using meteorological, PM2.5, and PM10 observational data, we screened the observed MLH automatically; the ceilometer observations were fairly consistent with the radiosondes, with a correlation coefficient greater than 0.9. Further analysis indicated that the MLH is low in autumn and winter and high in spring and summer in Beijing. There is a significant correlation between the sensible heat flux and MLH, and the diurnal cycle of the MLH in summer is also affected by the circulation of mountainous plain winds. Using visibility as an index to classify the degree of air pollution, we found that the variation in the sensible heat and buoyancy term in turbulent kinetic energy (TKE) is insignificant when visibility decreases from 10 to 5 km, but the reduction of shear term in TKE is near 70 %. When visibility decreases from 5 to 1 km, the variation of the shear term in TKE is insignificant, but the decrease in the sensible heat and buoyancy term in TKE is approximately 60 %. Although the correlation between the daily variation of the MLH and visibility is very poor, the correlation between them is significantly enhanced when the relative humidity increases beyond 80 %. This indicates that humidity-related physicochemical processes is the primary source of atmospheric particles under heavy pollution and that the dissipation of atmospheric particles mainly depends on the MLH. The presented results of the atmospheric mixing layer provide useful empirical information for improving meteorological and atmospheric chemistry models and the forecasting and warning of air pollution.

The implementation of emission reductions during the 2014 Asia-Pacific Economic Cooperation (APEC) summit provides a valuable opportunity to study air pollution in Beijing. From 15 October to 30 November 2014, the height of the atmospheric mixing layer and the vertical attenuated backscattering coefficient profiles were observed online using a~lidar ceilometer. Compared with fine particulate matter (PM2.5) and aerosol optical depth (AOD) data, the attenuated backscattering coefficients measured by the lidar ceilometer were strongly correlated with the PM2.5 concentration and AOD (correlation coefficients of 0.89 and 0.86, respectively). This result demonstrated the reliability of the vertical distribution of particles measured by the lidar ceilometer. By classifying different degrees of air pollution based on visibility, we found that during the transition period of air pollution, which was affected by transport of southerly flows in the mixing layer, the attenuated backscattering coefficient from 0 to 1500 m was enhanced by approximately 1.4 Mm−1 sr−1 (140 %). During the polluted period, the attenuated backscattering coefficient from 0 to 300 m suddenly increased, and the coefficient near the surface peaked (approximately 14 Mm−1 sr−1); however, the attenuated backscattering coefficient from 300 to 900 m decreased gradually, and the average value from 0 to 1500 m decreased by 0.5 Mm−1sr−1 (20 %). The height of the mixing layer gradually decreased, and the ratio of CO / SO2 gradually increased, which indicate that the polluted period was dominated by local contribution. Due to the emission reductions during APEC (DAPEC), the concentration of PM2.5 decreased by 59.2 and 58.9 % and visibility improved by 70.2 and 56.0 % compared to before (BAPEC) and after APEC (AAPEC), respectively. The contribution of regional transport in DAPEC decreased by approximately 36 and 25 %, and the local contribution decreased by approximately 48 and 54 % compared to BAPEC and AAPEC, respectively. The most effective method of controlling air pollution in the Beijing area is to reduce regional emissions during the transition period and reduce local emissions during the polluted period.

The evolution of physical, chemical and optical properties of urban aerosol particles was characterized during an extreme haze episode in Beijing, PRC, from 24 through 31 January 2013 based on in situ measurements. The average mass concentrations of PM1, PM2.5 and PM10 were 99 ± 67 μg m−3 (average ± SD), 188 ± 128 μg m−3 and 265 ± 157 μg m−3, respectively. A significant increase in PM1-2.5 fraction was observed during the most heavily polluted period. The average scattering coefficient at 550 nm was 877 ± 624 Mm−1. An increasing relative amount of coarse particles can be deduced from the variations of backscattering ratios, asymmetry parameter and scattering Ångström exponent. Particle number-size distributions between 14 and 2500 nm diameter showed high number concentrations, particularly in the nucleation mode and accumulation mode. Size-resolved chemical composition of submicron aerosol from a high-resolution time-of-flight aerosol mass spectrometer showed that the mass concentrations of organic, sulfate, nitrate, ammonium and chlorine mainly resided on particles between 500 and 800 nm (vacuum diameter), and nitrate and ammonium contributed greatly to particle growth during the heavily polluted day (28 January). Increasing relative humidity and stable synoptic conditions on 28 January combined with heavy pollution on 28 January, leading to enhanced water uptake by the hygroscopic submicron particles and formation of secondary aerosol, which might be the main reasons for the severity of the haze episode. Light-scattering apportionment showed that organic, sulfate, ammonium nitrate and ammonium chloride compounds contributed to light-scattering fractions of 54, 24, 12 and 10%, respectively. This study indicated that the organic component in submicron aerosol played an important role in visibility degradation during the haze episode in Beijing.

The decline of the woodland caribou population is a result of their habitat loss. To conserve the habitat of the woodland caribou and protect it from extinction, it is critical to accurately characterize and monitor its habitat. Conventionally, products derived from low to medium spatial resolution remote sensing data, such as land cover classification and vegetation indices are used for wildlife habitat assessment. These products fail to provide information on the structure complexities of forest canopies which reflect important characteristics of caribou’s habitats. Recent studies have employed the LiDAR system (Light Detection And Ranging) to directly retrieve the three dimensional forest attributes. Although promising results have been achieved, the acquisition cost of LiDAR data is very high. In this study, utilizing the very high spatial resolution imagery in characterizing the structural development the of forest canopies was exploited. A stand based image texture analysis was performed to predict forest succession stages. The results were demonstrated to be consistent with those derived from LiDAR data.

The objectives of this study were to exploit Light Detection And Ranging (LiDAR) and very high spatial resolution (VHR) data and their synergy with hyperspectral imagery in the early detection of the EAB presence in trees within urban areas and to develop a framework to combine information extracted from multiple data sources. To achieve these, an object-oriented framework was developed to combine information derived from available data sets to characterize ash trees. Within this framework, individual trees were first extracted and then classified into different species based on their spectral information derived from hyperspectral imagery, spatial information from VHR imagery, and for each ash tree its health state and EAB infestation stage were determined based on hyperspectral imagery. The developed framework and methods were demonstrated to be effective according to the results obtained on two study sites in the city of Toronto, Ontario Canada. The individual tree delineation method provided satisfactory results with an overall accuracy of 78 % and 19 % commission and 23 % omission errors when used on the combined very high-spatial resolution imagery and LiDAR data. In terms of the identification of ash trees, given sufficient representative training data, our classification model was able to predict tree species with above 75 % overall accuracy, and mis-classification occurred mainly between ash and maple trees. The hypothesis that a strong correlation exists between general tree stress and EAB infestation was confirmed. Vegetation indices sensitive to leaf chlorophyll content derived from hyperspectral imagery can be used to predict the EAB infestation levels for each ash tree.