Nano-Structural Superwetting Surfaces for Highly Reliable On-Site Detection of Bisphenol A.

In the domain of big data geographic screening for environmental pollutants, the expeditious dissemination of testing results to environmental investigation professionals is pivotal in facilitating comprehensive analysis and the implementation of more efficacious strategies for managing environmental issues. However, this endeavor can prove to be particularly arduous when conducting examinations in remote, resource-scarce rural areas and field environments, where deficient infrastructure often emerges as the principal impediment to unimpeded environmental monitoring. Therefore, the development of a reliable and portable monitoring strategy with the ability to analyze large amounts of data is highly required. Here, a deep-learning (DL)-assisted portable sensing strategy was developed based on thermal and pH dual-responsive nano-structural superwetting surfaces, for highly reliable, quick, and field monitoring of environmental pollutants. In our experiment, bisphenol A (BPA) was selected as the representative pollute. The achieved limit of detection, attaining a remarkably low value of 1.05 µM, unequivocally adhered to stringent international testing standards for evaluating the migration of BPA in thermal paper. Based on a DL image classification algorithm, highly precise predictions regarding the migration of BPA concentration were achieved, with an accuracy rate exceeding 99%. Furthermore, it successfully facilitated automated and exceedingly reliable monitoring of the migration of BPA from thermal paper within the principal provinces of thermal paper production in China. This strategy engenders the potential to establish correlations between environmental pollutant concentrations in specific regions and the prevalence of certain human ailments.

Regional climate change and possible causes over the Three Gorges Reservoir Area.

The Three Gorges Project, the largest hydroelectric project in the world, has attracted widespread attention regarding its impact on regional climate. However, existing studies on the climate effects of the Three Gorges Project construction are not sufficient due to limited data accumulation. In this study, we analyzed the annual and seasonal trend changes in temperature, precipitation, and humidity over the Three Gorges Reservoir Area (TGRA) based on long-term meteorological stations data, remote sensing data, and reanalysis products. Observation minus reanalysis method (OMR) was used to reveal possible impacts of land cover changes on climate changes. Major results indicated that the TGRA experienced an overall warming trend for both annual and seasonal variations, with greater rising trends in the upstream. Except for autumn, the relative humidity of most regions mainly showed significant downward trends, indicating an overall drying trend in the TGRA. There was insignificant change in total precipitation and precipitable water vapor, with the largest variation observed during the summer. Although there were small differences among these datasets, their results of climate changes showed good consistency overall. In addition, the results of OMR indicated that land cover changes mainly had a warming and drying effect on the middle and upper reaches, and a cooling and moistening effect on the lower reaches of the TGRA. This may be due to the impact of land cover changes on the surface energy balance, thus affected temperature and humidity. The study has important reference value for understanding the climate changes in the TGRA and the climate effects brought about by large-scale engineering construction.

Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales.

MODIS and VIIRS aerosol products have been used extensively by the scientific community. Products in operation include MODIS Dark Target (DT), Deep Blue (DB), and Multi-Angle Implementation of Atmospheric Correction (MAIAC) and VIIRS DT, DB, and NOAA Environmental Data Record products. This study comprehensively validated and inter-compared aerosol optical depth (AOD) and Ångstrom exponent (AE) over land and the ocean of these six products (seven different algorithms) on regional and global scales using AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) observations. In particular, we used AERONET inversions to classify AOD and AE biases into different scenarios (depending on absorption and particle size) to obtain retrieval error characteristics. The spatial patterns of the products and their differences were also analyzed. Collectively, although six satellite AODs are in good agreement with ground observations, VIIRS DB (land and ocean) and MODIS MAIAC (land only) AODs show better validation metrics globally and better performance in 8/10 world regions. Therefore, they are more recommended for usage. Although land AE retrievals are not capable of quantitative application at both instantaneous and monthly scales, their spatial patterns show qualitative potential. Ocean AE shows a relatively high correlation coefficient with ground measurements (>0.75), meeting the fraction of expected accuracy (> 0.70). Error characteristic analyses emphasize the importance of aerosol particle size and absorption-scattering properties for land retrieval, indicating that improving the representation of aerosol types is necessary. This study is expected to facilitate the usage selection of operating VIIRS and MODIS products and their algorithm improvements.

Assessment of solar energy potential in China using an ensemble of photovoltaic power models.

Development of solar energy is one of the key solutions towards carbon neutrality in China. The output of solar energy is dependent on weather conditions and shows distinct spatiotemporal characteristics. Previous studies have explored the photovoltaic (PV) power potential in China but with single models and low-resolution radiation data. Here, we estimated the PV power potential in China for 2016-2019 using an ensemble of 11 PV models based on hourly solar radiation at the resolution of 5 km retrieved by the Himawari-8 geostationary satellite. On the national scale, the ensemble method revealed an annual average PV power potential of 242.79 kWh m-2 with the maximum in the west (especially the Tibetan Plateau) and the minimum in the southeast (especially the Sichuan Basin). The multi-model approach shows inter-model spreads of 6 %-7 % distributed uniformly in China, suggesting a robust spatial pattern predicted by these models. The seasonal variation in general shows the largest PV power generation in summer months except for Tibetan Plateau, where the peak value appears in spring because the high cloud coverage dampens the regional solar radiation in summer. On the national scale, the deseasonalized PV power potential shows a high correlation with cloud coverage (R2 = 0.71, p < 0.01) but a low correlation with aerosol optical depth (R2 = 0.08, p < 0.05). Sensitivity experiments show that national PV power potential increases by 0.55 % per 1 W m-2 increase of radiation and 0.79 % per 1 m s-1 increase of wind speed, but decreases by 0.46 % per 1 °C increase of air temperature. These sensitivities provide a solid foundation for the future projection of PV power potential in China under climate change.

Prediction of diffuse solar radiation by integrating radiative transfer model and machine-learning techniques.

Diffuse radiation is a major component of solar radiation that is important in carbon exchanges and material, energy, and information flows in agricultural ecosystems; however, measuring diffuse radiation is difficult and expensive, leaving only few stations in China that can record diffuse radiation. Therefore, five high-speed and highly accurate hybrid models were developed and compared to simulate diffuse radiation based on the aerosol optical properties and radiation parameters provided by the Aerosol Robotic Network (AERONET), Baseline Surface Radiation Network (BSRN), Wuhan University, Chinese Ecosystem Research Network (CERN), GLASS surface albedo data, and combined radiative transfer model (RTM) with machine learning (ML) models that include random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron (MLP), deep neural networks (DNN), and convolutional neural network (CNN). Furthermore, the uncertainty in the simulated diffuse radiation due to the measurement uncertainties of aerosol optical properties and land surface albedo was quantified, and the relative contributions of multiple variables to diffuse radiation were analyzed. The results showed that RTM-RF was the most successful, with determination coefficients (R2) of 0.95, 0.94, and 0.98, and minimum root mean square errors (RMSE) of 9.56, 10.05, and 13.27 W m-2 at the Lulin, Wuhan, and Xianghe sites, respectively. The largest measurement uncertainty in the aerosol optical depth (AOD) was found at the Lulin site, while that of the single-scattering albedo led to the largest errors in Wuhan and Xianghe. AOD, solar zenith angle (SZA), and single-scattering albedo contributed significantly more than the asymmetry factor, land surface albedo, precipitable water vapor, and ozone. This was especially true for AOD, which was higher than 28 % at all sites. Overall, the proposed RTM-RF method exhibited superior performance, therefore we recommend it for estimating diffuse radiation in China.

Characteristics of the extreme precipitation and its impacts on ecosystem services in the Wuhan Urban Agglomeration.

Summer extreme precipitation is one of the most frequent, intense, and influential extreme weather events that occurs frequently in the Wuhan Urban Agglomeration (WUA). Preventing meteorological disasters and coping with climate change necessitate understanding the characteristics and causes of extreme precipitation and its impact on ecosystems. In this study, the spatiotemporal characteristics of summer extreme precipitation in the WUA are analysed from 1961 to 2020. Then, NCEP reanalysis data and the relevant circulation index are used to explore the causes of extreme precipitation. Finally, how extreme precipitation influences key ecosystem services, such as water yield, water regulation, and soil conservation, is investigated. The results reveal that (1) extreme precipitation in the WUA has shown an obvious upwards trend over the past 60 years. Huanggang, Xianning, Huangshi, Wuhan, and E'zhou city demonstrate the highest values. The extreme precipitation increased significantly after 1980s, especially the R97.5P and PRCPTOT with change rate of 12.1 mm/10a and 18.82 mm/10a respectively. (2) Atmospheric circulation variation is a dominant factor affecting extreme precipitation in the WUA and causes the meridional distribution of the "+ - +" wave train in eastern China. The intensity and location of the Western Pacific subtropical high are closely related to extreme precipitation. Furthermore, the weakening of the East Asian summer monsoon circulation is also conducive to the occurrence of extreme precipitation. (3) The spatial distribution of water yield and runoff retention in abnormal extreme precipitation years are similar to the variation patterns of the total amount of extreme precipitation. Water yield and runoff retention in high-value extreme precipitation years are higher than that in low-value extreme precipitation years, while soil conservation shows no difference. In addition, ecosystem services have a synergistic relationship in high-value areas and a trade-off relationship in low-value areas. This study can contribute to the understanding of extreme precipitation in the WUA and its interaction with ecosystem services.

Climate change impact on photovoltaic power potential in China based on CMIP6 models.

China has the largest worldwide cumulative installed photovoltaic (PV) capacity, which is expected to be 1300 GW in 2050. Industrial production, population explosion and fossil fuel combustion would reduce the surface solar radiation that could be received by PV panels. However, it is still a problem to explore the integrated effects of socio-economic and air pollutant emissions on PV power potential in China. In this study, climate change impact on PV power potential in 2023-2100 were assessed using the Coupled Model Intercomparison Project Phase 6 (CMIP6) model, combining Shared Socio-economic Pathway (SSPs) and Representative Concentration Pathways (RCPs). The validation results with ground-based surface solar radiation measurements collected from 17 China Meteorological Administration (CMA) stations showed that the Meteorological Research Institute Earth System Model version 2-0 (MRI-ESM2-0) attained a better performance with mean correlation coefficients (R), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) of 0.85, 35.80 Wm-2 and 29.37 Wm-2, respectively. Then, the MRI-ESM2-0 model was selected to analyze the spatial and temporal variations in PV power potential. PV power potential decreased significantly in SSP585 ranging from 192.71 Wm-2 to 189.96 Wm-2 in 2023-2100 corresponding to the growing resource intensity and fossil fuel dependency. In contrast, if China continues on the path of sustainable and low-carbon development and keeps temperature rise to about 1.5 °C by 2100, PV power potential will increase by 1.36-5.90 Wm-2. Meanwhile, the effects of climatological factors on PV power potential were analyzed by Empirical Orthogonal Function (EOF) method. Results indicated that surface solar radiation had the highest contribution of >50 %, and the contribution of aerosols and cloud cover was about 20 %. This study is conducive to the full utilization of solar resources and has important implications for the future formulation of solar energy policy in China.

Retrieval of total and fine mode aerosol optical depth by an improved MODIS Dark Target algorithm.

Total and fine mode aerosol optical depth (AODT and AODF), as well as the fine mode fraction (FMF = AODF/AODT), are critical variables for climate change and atmospheric environment studies. The retrievals with high accuracy from satellite observations, particularly FMF and AODF over land, remain challenging. This study aims to improve the Moderate-resolution Imaging Spectro-radiometer (MODIS) land dark target (DT) algorithm for retrieving AODT, AODF, and FMF on a global scale. Based on the fact that the underestimated surface reflectance (SR) could overestimate the AODT and underestimate the aerosol size parameter in the DT algorithm, two robust schemes were developed to improve SR determination: the first (NEW1 DT) used the top of the atmosphere reflectance instead of SR at 2.12 µm; the second (NEW2 DT) used eleven-year MODIS data to establish a monthly spectral SR relationship model (2.12-0.47 and 2.12-0.65 µm) database at pixel-by-pixel scale. Then a novel lookup table approach based on the physical process was proposed to retrieve the AODF and FMF. The new MODIS AODT, FMF, and AODF were compared to AERosol RObotic NETwork (AERONET) retrievals. Results showed that the root mean square error (RMSE) was 0.096-0.103, 0.098-0.099, and 0.167-0.180 for the new AODTs, AODFs, and FMFs, respectively, which were better than that of the Collection 6.1 (C6.1) DT (0.117, 0.235, and 0.426) in the validation by global AERONET sites. From the validation results, NEW2 DT provided better AODT and coarse mode AOD retrievals, while NEW1 DT had better AODF and FMF performances. The spatial patterns of AODF, FMF, and AODC of the new DT algorithms were comparable to those of the Polarization and Directionality of the Earth's Reflectances aerosol product. Hence, the new algorithms have the potential to provide global AODT, FMF, and AODF products over land to the scientific community with high accuracy using long-term MODIS data.

A method for improving the estimation of extreme air temperature by satellite.

Air temperature (Ta) data obtained from meteorological stations were spatially discontinuous. Some satellite data have complete spatial coverage and strong relationships with Ta (e.g., elevation and land surface temperature). Therefore, Ta can be mapped using in situ Ta and satellite data. However, this method may have a large bias when estimating the extreme Ta. In this study, the error prediction and correction (EPC) method, incorporating Cubist machine learning algorithm, was proposed to improve the estimation of extreme Ta. The accuracy of the EPC method was compared with that of the widely used method in previous studies in east China from 2003 to 2012. The mean absolute errors (MAEs) of the estimated daily Ta using the EPC method ranged from 0.75-1.01 °C, which were 0.57-0.96 °C lower than that of the method in the literature. The biases of the estimated Ta obtained using the two methods were close to zero. However, the biases can be as high as 7.10 °C when Ta is extremely low and as low as -3.09 °C when Ta is extremely high. Compared with the method in the literature, the EPC method can reduce the MAE by 1.41 °C, root mean square error by 1.49 °C, and bias by 1.61 °C of the estimated extreme Ta. Additionally, the EPC method produced satisfactory accuracy (MAEs <0.9 °C) of the estimated heat and cold wave magnitudes. Finally, a 1 km resolution daily Ta map in east China from 2003 to 2012 was developed, which will be useful data in multiple research fields.

Accuracy, stability, and continuity of AVHRR, SeaWiFS, MODIS, and VIIRS deep blue long-term land aerosol retrieval in Asia.

The deep blue (DB) aerosol algorithm applied to four satellite instruments, AVHRR, SeaWiFS, MODIS, and VIIRS, produced a long-term aerosol data set since 1989. This study first evaluated and compared the accuracy, stability, and continuity of four DB aerosol optical depth (AOD) products in Asia using AErosol RObotic NETwork measurements. Then, the regional AOD spatial distributions, coverages, and series trends are analyzed. The results show that VIIRS DB has the highest accuracy and stability, with an expected error (EE, ±(0.05 + 20%)) of 76.59% and stability of approximately 0.027 per decade. The performance of MODIS DB is slightly worse than that of VIIRS. However, their AOD pattern, coverage, and trend are comparable. The performance of AVHRR (EE = 58.10%) and the stability of SeaWiFS (0.093 per decade) are less good. Therefore, SeaWiFS DB data should be used with caution for trend analysis. The AOD accuracy and coverage together determine the AOD pattern and the continuity of multi-sensor data. In addition to consistent algorithm accuracy, it is necessary to consider the influences in sensor sampling and inappropriate-pixel screening schemes in the joint multi-sensor analysis. Encouragingly, although multiple DB products have different AOD averages of regional series, their changing trends are consistent. Error analysis shows that the AOD bias characteristic is different in different surface conditions. This indicates that the surface reflectance estimated by the DB algorithm using different techniques is divergent, which may be the direction for the improvement of the algorithm.

Spatiotemporal heterogeneities and driving factors of water quality and trophic state of a typical urban shallow lake (Taihu, China).

Water quality deterioration and eutrophication of urban shallow lakes are global ecological problems with increasing concern and greater environmental efforts. In this study, spatiotemporal changes of water quality and eutrophication were assessed by trophic level index (TLI), cluster analysis, and spatial interpolation methods in Lake Taihu and its sub-lakes from 2015 to 2019. Results showed that the Taihu had poor water quality and maintained a light-eutropher state overall, mainly astricted by the total nitrogen (TN) and the total phosphorus (TP). All nutrient parameters reached relatively higher concentrations in the northwestern and northern areas. Meiliang Bay was the most polluted and nutrient-rich area. In terms of trend, the Mann-Kendall test highlighted that the TP and chlorophyll-a (Chl-a) concentrations increased significantly while the TN and five-day biochemical oxygen demand (BOD5) decreased. The massive nutrient loads caused by human activity from the northwestern Taihu and the geomorphological characteristic of the north closed bays were the main contributors to the spatial heterogeneity in water quality. The main driving force of the alleviative nitrogen pollution was the declining river inflow nitrogen loading, and phosphorus pollution was affected more by accumulated endogenous pollution and decline in aquatic plants area, as well as closely linked with algae biomass. Further water pollution and eutrophication restoration of Taihu should focus on the nutrient reductions and those heavily polluted closed bays.

Spatiotemporal distributions of pan evaporation and the influencing factors in China from 1961 to 2017.

Pan evaporation (EVP) is an important element of the hydrological cycle and exhibits a close relationship with climate change. In this study, the generalized regression neural network (GRNN) model and extreme gradient boosting (Xgboost) model were applied to estimate the monthly EVP. The spatiotemporal distributions of EVP and influencing factors in China and eight subregions from 1961 to 2017 were analyzed. The root mean square error (RMSE) of all GRNN models was approximately 10%, and the Nash-Sutcliffe efficiency (NSE) coefficient was larger than 0.94 in different subregions. The annual mean EVP in all subregions and throughout China showed decreasing trends before 1993, while EVP increasing trends occurred in East China (EC), South China (SC), Southwest China (SWC), west of Northwest China (WNC), and throughout China after 1994. Subsequently, the variable importance in projection (VIP) between EVP and climatic factors obtained by partial least squares (PLS) regression and the relative contribution calculated by Xgboost stepwise regression analysis (SRA) were used to investigate the climatic parameter sensitivity to EVP. The results indicated that the combined effects of the vapor pressure deficit (VPD), sunshine duration (SSD), and wind speed (WIN) were the main reasons for the variations in EVP across China. At the seasonal scale, SSD, WIN, relative humidity (RHU), and VPD were the most sensitive climatic factors to EVP in different seasons. In addition, the Pacific decadal oscillation (PDO) index showed a significant negative correlation with EVP, and the El Niño 3.4 (N3.4) and East Atlantic/Western Russia (EA/WR) indices revealed positive correlations in most regions from 1961 to 1993, while the North Atlantic oscillation (NAO) was negatively correlated with EVP. Moreover, N3.4 and Atlantic multidecadal oscillation (AMO) were positively correlated with EVP from 1994 to 2017. Finally, the yearly number of heatwave events (HWN) was highly correlated with EVP because of the high VPD and SSD levels during the heatwave event periods.

Accuracy and error cause analysis, and recommendations for usage of Himawari-8 aerosol products over Asia and Oceania.

The Himawari-8 aerosol algorithm was updated to version 3 (V30). However, no study has evaluated its performance. The purpose of this study is to verify and to compare version 2.1 (V21) and V30 aerosol products, to explain which factor dominates the aerosol optical depth (AOD) error, and to provide recommendations for aerosol product usage. The AOD accuracy of V30 was better than that of V21, with a higher correlation coefficient (R) and a higher expected error (EE_DT). The V30 AOD metrics (including R, EE_DT, and the root mean square error) exceeded those of V21 on more than 69% of the AERONET sites and its bias from MODIS AOD was smaller than that of V21 AOD. However, the V30 AOD does not meet the metric of EE_DT > 0.66. The analysis results suggest that aerosol type parameters (primarily the Ångström exponent (AE)) may be the dominant factor determining the AOD error. This reveals the direction of H8 algorithm improvement. More than 59% of the H8 AE value meets the expected error but they do not capture the variety (R < 0.3). The FMF and SSA retrieved by H8 performed poorly. The V30 AOD performs best in Japan and South Korea (83.3% of AERONET sites meet the EE_DT > 0.66 requirement) and has better data accuracy in the morning. Therefore, we recommend V30 AOD morning data to users in Japan and South Korea regions.

Environmental factors modulate the diffuse fertilization effect on gross primary productivity across Chinese ecosystems.

Diffuse radiation allocated by cloud cover and aerosols can promote vegetation photosynthesis, which is known as the diffuse fertilization effect (DFE). As an important uncertain factor regulating the DFE, understanding the role of environmental conditions in the response of terrestrial ecosystems to diffuse radiation is vital for quantitative and intensive studies. By using a light use efficiency model and statistical methods with satellite data and ChinaFLUX observation data, the optimal environmental range of DFE was estimated, the indirect role of vapor pressure deficit (VPD) and air temperature (Ta) on DFE was explored, and the relative contribution of diffuse photosynthetically active radiation (PARdif) on gross primary productivity (GPP) was analyzed across Chinese ecosystems under different sky conditions. The results showed that the DFE increased with leaf area index (LAI), but distributed a unimodal curve along with VPD and Ta, both of which had an optimum range that was lower in the forest (or cropland) and higher in the grass (or desert) ecosystem. When considering the co-effect of VPD and Ta, the strongest positive effect of DFE was found at 0-5 h Pa and 20-25 °C. Based on path analysis, PARdif promoted GPP and served as the main controlling factor in forest ecosystems predominantly through a direct pathway from half-hourly to the daily scale, while Ta and VPD occupied the dominant position at single-canopy ecosystem sites. When the aerosol optical depth (AOD) increased, the relative contribution of PARdif increased in multiple-canopy ecosystems and decreased in single-canopy ecosystems; when the sky conditions changed from sunny to cloudy, the relative contribution of PARdif was higher in the forest ecosystem and increased significantly in the grass ecosystem. These findings offer a more comprehensive understanding of the environmental effects of regulating DFE on GPP across ecosystems.

Estimation of the losses in potential concentrated solar thermal power electricity production due to air pollution in China.

The concentrated solar thermal power (CSP) industry is projected to expand rapidly in China in the next 30 years. However, anthropogenic aerosol emissions reduce direct radiation (Rdir) reaching the surface, resulting in the losses of potential CSP electricity production in China. In this study, we applied various models to estimate daily Rdir, and the results showed that the gradient boosting with categorical features support (CatBoost) model was superior to other models, and coefficient of determination (R), root mean square error (RMSE) and mean absolute error (MAE) were 0.96, 1.99 MJ m-2 day-1 and 1.92 MJ m-2 day-1, respectively. We used Rdir data set at 839 stations across China derived by CatBoost model to calculate losses of the potential CSP electricity production from aerosol emissions. The results showed that the potential CSP electricity production decreased by 12.9% (136 kWh) on average at provincial level during 1961-2015. It is plausible that air quality will continue to improve from now due to the success of previous air pollution control measurements and the commitment to the United Nations of "Carbon Neutrality". It was found that returning to direct radiation levels in 1960s could yield a 15.8% increase in potential CSP electricity production, equal to 28.4-79 TWh with the expected 2050 CSP installation capacities. The corresponding economic benefits could reach 17.1-56.9 billion RMB in 2050. The findings in this study will be beneficial for siting, designing and optimizing CSP systems in China.

Assessing the ecological balance between supply and demand of blue-green infrastructure.

Given that improving urban ecological environment requires a clear recognition of the urban ecological elements, investigating the ecosystem service capabilities of urban green-blue infrastructures (UGBIs) becomes ever important. This study aims to reveal and compare the synergistic ecosystem service ability of UGBIs with different characteristics and the relationship with human demand in Wuhan city. It was found that the climate regulation service and water regulation service value of lake-type parks both reached the highest over the other UGBIs. Nature-type parks revealed the most capable cultural service, and green-type parks demonstrated the greatest exercise cultural service value. The analysis showed that the ecosystem services delivered by the UGBIs were influenced by the park area, the total value of the normalized difference vegetation index and normalized water body index, and the distance from the city centre. Furthermore, a significant spatial phenomenon was found that the ecological capacity of lake-type parks in the city centre was higher than that of the other UGBIs at the same location. Regarding the relationship with the human activity intensity, the high-demand and high-supply regions were mainly concentrated in highly developed areas in terms of regulating services. Nevertheless, a severe environmental inequality occurred in small urban centres, which requires urgent attention from the government. This work answered the question of where and how to optimize the green-blue infrastructures in Wuhan, and it contributes to the construction of the existing blue-green space.

Long-term trends of surface and canopy layer urban heat island intensity in 272 cities in the mainland of China.

The canopy layer urban heat island (CLUHI) and surface urban heat island (SUHI) refer to higher canopy layer and land surface temperatures in urban areas than in rural areas, respectively. The long-term trends of CLUHIs are poorly understood at the regional scale. In this study, 1 km resolution air temperature (Ta) data for the 2001-2018 period in the mainland of China were mapped using satellite data and station-based Ta data. Subsequently, the temporal trends of the CLUHI and SUHI intensities (CLUHII and SUHII, respectively) were investigated in 272 cities in the mainland of China. The Ta was estimated with high accuracy, with a root mean square error ranging from 0.370 °C to 0.592 °C. The CLUHII and SUHII increased significantly in over half of the cities in spring and summer, over one-third of the cities in autumn, and over one-fifth of the cities in winter. The trends of the nighttime SUHII were strongly related to the CLUHII calculated using mean and minimum Ta (correlation coefficients ranging from 0.613 to 0.770), whereas the relationships between the trends of the daytime SUHII and CLUHII were relatively weak. Human activities were the major driving forces for the increase in the CLUHII and SUHII. The difference in impervious surfaces between urban and rural areas was significantly correlated with the CLUHII and SUHII in approximately half of the cities. Meteorological factors were significantly correlated with the CLUHII and SUHII in few cities. This study highlights the trends of the significant increase in the CLUHII and SUHII in the mainland of China, which may have negative effects on humans and the environment.

Effect of urban expansion on atmospheric humidity in Beijing-Tianjin-Hebei urban agglomeration.

Accelerated urban expansion has contributed to the urban-rural contrast regarding atmospheric humidity. However, the effect of urban expansion on atmospheric humidity is not understood well in the Beijing-Tianjin-Hebei urban agglomeration (BTHUA). In this study, observations from 133 meteorological stations were used to analyze the long-term trend of atmospheric humidity and the urban expansion effect in the BTHUA during the period 1961-2014. The urban expansion effect on atmospheric humidity was evaluated by calculating the differences in atmospheric humidity trends between urban and rural series based on the dynamic classification method using secular urban impervious data. The results revealed that a drying trend of annual and seasonal average atmospheric humidity was observed in the urban areas of the BTHUA during the period 1961-2014, characterized by decreasing relative humidity (RH), water vapor pressure (Ea), specific humidity (Q) and increasing vapor pressure deficit (VPD). A more prominent drying trend (p < 0.05) appeared in spring and autumn, whereas a relatively weaker trend occurred in summer and winter. The trend of atmospheric humidity was significantly correlated (Spearman correlation coefficients: -0.45, 0.48, -0.29 and -0.32 for RH, VPD, Ea and Q, respectively; p < 0.01) with the urban expansion rate. The effect of urban expansion on the trend of VPD, Ea and Q was the strongest in spring at 0.138 hpa, -0.237 hpa and -0.151 hpa per decade, respectively, while the urban expansion effect on RH was the strongest in winter, reaching -1.159% per decade. This study provides a better understanding of the relationship between variations in atmospheric humidity and urban expansion, as well as scientific support for urban planning.

Impacts of preseason drought on vegetation spring phenology across the Northeast China Transect.

Vegetation phenology is undergoing profound changes in response to the recent increases in the intensity and frequency of drought events. However, the mechanisms by which drought affects the start of the growing season (SGS) are poorly understood particularly in arid and semi-arid regions. Here, we identified varying degrees of preseason drought events and analyzed the sensitivity of the SGS to preseason drought across the Northeast China Transect (NECT). Our results showed that drought caused a delayed SGS in grassland ecosystems, but an advanced SGS within forest ecosystems. These contrasting responses to preseason drought reflected different adaptive strategies between vegetation types. The SGS was shown to be highly sensitive to short timescales drought (1-3 months) in semi-arid grasslands where annual precipitation is 200-300 mm (i.e. SAGE200-300). Biomes within this region were found to be most vulnerable out of all the ecosystems to drought. Given the frequent nature of droughts in the mid-latitudes, a drought early warning system was recommended accompanied by improved modeling of how the SGS will be affected by intensified drought under future climate change.

A novel efficient broadband model to derive daily surface solar Ultraviolet radiation (0.280-0.400 µm).

The climatic characteristics of solar Ultraviolet radiation (UV) are of vital important for the climate change and photochemical reactions. High-quality records of solar UV radiation are the premise for solar UV researches and applications, but solar UV radiation observations are sparse around the world. Among all wavelength of UV radiations, only UVA (0.315-0.400 nm) and UVB (0.280-0.315 nm) could reach the earth surface. This study attempted to develop a novel efficient physically broadband parameterization (hereafter, FASTUV) for estimating surface solar UV radiation (0.280-0.400 µm) in all-sky conditions based on Leckner's spectral model for calculating shortwave solar radiation, using MERRA_2 reanalysis data. The Quadratic polynomial formula and artificial neural networks were used to calculate the cloud transmittance for UV, using sunshine durations measurements at 2474 CMA stations. The surface solar UV radiation measurements at 29 CERN (The Chinese Ecosystem Research Network) stations were used for validating the estimated UV values. The result showed the FASTUV model could be used for estimating UV values with high accuracy, strong robustness and fast speed. Then, the spatial and temporal variation of surface solar UV radiation in China were revealed. The result indicated that the Qinghai Tibetan Plateau and the Palmier Plateau has always been the areas with highest UV values, while the Northeastern China is the area with the lowest UV values. Meanwhile, the FASTUV model have been packaged into a software namely 'FASTUV_V1.0'. We provide the executable file of FASTUV model in publicly available repository: https://doi.org/10.6084/m9.figshare.11409666.