Spatial Distribution Trends Research Articles

Estimation of Terrestrial Net Primary Productivity in the Yellow River Basin of China Using Light Use Efficiency Model

The net primary productivity (NPP) of vegetation is an essential factor of ecosystem functions, including the biological geochemical carbon cycle, which is often impacted by climate change and human activities. It plays a significant role in comprehending the nature of carbon balance in an ecosystem and demonstrates the global and regional carbon cycle dynamics. The present study used an upgraded CASA model to calculate the NPP in the Yellow River Basin (YRB), China. The model’s simulation ability was improved by changing the model parameters. Further, the CASA model was validated by comparing with MODIS-NPP and in situ observed NPP, wherein the accuracy of the CASA model estimation was found satisfactory to estimate NPP changes in the study area. The simulated results of the improved CASA model showed that the mean annual NPP value of vegetation in the YRB was 283.4 gC m–2 a–1 from 2001 to 2020, with a declining trend in spatial distribution from south to north. In contrast, the NPP appeared as an increasing trend in the YRB temporally from 212 gC m–2 a–1 in 2001 to 342 gC m–2 a–1 in 2020, with a mean annual growth rate of 4.6 gC m–2 a–1. The total NPP in the YRB increased by 40,088.3 GgC between 2001 and 2020, from 226.06 TgC to 266.15 TgC. This rise can be attributed to the increase in forests. The average grassland area has reduced by 4651 km2 during the last two decades, significantly impacting the total NPP of grasslands. Although the increase in NPP in wetlands was minimal, accounting for 815.53 GgC, the highest change percentage of 79.78%, could be observed among the six vegetation types due to the anthropogenic influences and climate change. The conditions favorable for vegetation growth and a sustained environment were enhanced by the increased precipitation and temperature and the reinforced ecological protection by the government.

Analyses of Spatial Distribution and Temporal Trends of Temperature and Its Extremes over Nigeria using Climate Indices

Climate change and its associated extreme climate and extreme events are threat to man and his environment. Over the years, climatic parameters have been used as climate change indicators to study climate change. In order to access extreme climate due to climate change in Nigeria, this paper attempt to evaluate spatial distribution and temporal trends of temperature and its extremes over Nigeria for 35 years (1979 - 2013). Daily temperature data used were obtained from the Nigeria Meteorological Agency (NIMET), Lagos. The data was subjected to quality checked and homogenization. Climate extreme indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI) was adopted in this study. The data were analysed employing Sen’s slope estimator and Mann-Kendall test. Results revealed that spatial distribution of temperature and its extremes over Nigeria varied across the country, increasing/decreasing mostly towards the northern or the coastal regions. Significant increase was observed at p < 0.05 in the temporal trends of averages of yearly maximum and minimum temperature and hot temperature indices in all the regions in line with the global warming trend. The increase in warm days and warm nights doubled the rate at which cold days and cold nights decreased in most of the study areas. The annual and decadal changes in temperature and its extremes as well as the period under investigation (35 years) were determined. Our findings depict that the country is trending towards a drier and warmer climate which are clear evidence of climate change. The results further suggest that the Atlantic Ocean and the Sahara Desert which control the atmospheric condition of the country may be promoter of the observed extreme events caused by climate change in Nigeria. This study discussed contemporary issues in our society which are useful to the general populace, researchers, and policy makers among others.
 HIGHLIGHTS
 
 Spatial distribution of temperature and its extremes over Nigeria varied across the country from one region to another
 Maximum and minimum temperature and hot temperature indices increased significantly (p < 0.05) in all the regions in line with the global warming trend
 The increase in warm days/nights doubled the rate at which cold days/cold nights decreased in most of the regions
 Our findings suggest that Atlantic Ocean and Sahara Desert which control the atmospheric conditions of Nigeria may promote extreme events caused by climate change in Nigeria
 
 GRAPHICAL ABSTRACT

An Integrated Algorithm for Extracting Terrain Feature-Point Clusters Based on DEM Data

Terrain feature points, such as the peaks and saddles, are the basic framework of surface topography and its undulations, which significantly affect the spatial distribution of surface topography. In the past, terrain feature points were extracted separately for each type, while the internal connections between the terrain feature points were ignored. Therefore, this work proposes an integrated algorithm for extracting terrain feature-point clusters, including the peaks, saddles and runoff nodes, based on the DEM data. This method includes two main processes: positive terrain-constrained ridgeline extraction and terrain feature-point cluster extraction. Firstly, a threshold determination method of flow accumulation in the hydrological analysis is proposed by combining morphological characteristics with runoff simulation, and the ridgelines are extracted based on this threshold. Subsequently, the peaks and their control areas are extracted by space segmentation. Meanwhile, the saddles and runoff nodes are obtained by spatial intersection. Finally, the integrated terrain feature-point clusters are obtained by merging the three extracted terrain feature points. This method was experimented with in the six typical sample areas in Shaanxi Province and verified its results by contour lines and optical images. It shows that the spatial positions of the extracted terrain feature clusters are accurate, and the coupling relationships are great. Finally, the experiments show that the statistical attributes of point clusters and their spatial distribution trends have an obvious correlation with geomorphic types and geomorphic zoning, which can provide an important reference for geomorphic zoning and mapping.

Spatiotemporal neural network for estimating surface NO2 concentrations over north China and their human health impact

Atmospheric nitrogen dioxide (NO2) is an important reactive gas pollutant harmful to human health. The spatiotemporal coverage provided by traditional NO2 monitoring methods is insufficient, especially in the suburban and rural areas of north China, which have a high population density and experience severe air pollution. In this study, we implemented a spatiotemporal neural network (STNN) model to estimate surface NO2 from multiple sources of information, which included satellite and in situ measurements as well as meteorological and geographical data. The STNN predicted NO2 with high accuracy, with a coefficient of determination (R2) of 0.89 and a root mean squared error of 5.8 μg/m3 for sample-based 10-fold cross-validation. Based on the surface NO2 concentration determined by the STNN, we analyzed the spatial distribution and temporal trends of NO2 pollution in north China. We found substantial drops in surface NO2 concentrations ranging between 9.1% and 33.2% for large cities during the 2020 COVID-19 lockdown when compared to those in 2019. Moreover, we estimated the all-cause deaths attributed to NO2 exposure at a high spatial resolution of about 1 km, with totals of 6082, 4200, and 18,210 for Beijing, Tianjin, and Hebei Provinces in 2020, respectively. We observed remarkable regional differences in the health impacts due to NO2 among urban, suburban, and rural areas. Generally, the STNN model could incorporate spatiotemporal neighboring information and infer surface NO2 concentration with full coverage and high accuracy. Compared with machine learning regression techniques, STNN can effectively avoid model overfitting and simultaneously consider both spatial and temporal correlations of input variables using deep convolutional networks with residual blocks. The use of the proposed STNN model, as well as the surface NO2 dataset, can benefit air quality monitoring, forecasting, and health burden assessments.

Integrated Zoning Protection of Urban Remains from Perspective of Sustainable Development—A Case Study of Changchun

In recent years, the development of urbanization in China has entered a new stage with “quality” as its core. In the process of sustainable urban development, urban planning and construction relics are precious historical warnings and educational resources, and in the stage of accelerated urbanization, social transformation, and industrial upgrading, urban sites face severe pressure and challenges for conservation. This paper presents a summary analysis of various international charters concerning historical and cultural heritage in recent times, and analyzes the urban spatial structure, urban functional zoning, and the differences and evolutionary characteristics of urban construction sites between the pseudo-Manchukuo Xinjing plan and other regions of the world in the same period from a global perspective. The city of Changchun is also used as an example to systematically analyze the existing relics in Changchun using a historical-geographical perspective and spatial analysis methods. The results show that, firstly, the conservation objects of Chinese relics are being enriched and the scope of protection is being expanded, but that there is a lack of protection and utilization of urban heritage and its surrounding environment. Secondly, that the road network system and spatial structure of the pseudo-Manchukuo Xinjing city planning are basically preserved, that the urban green space and functional zoning have been changed, and that the architectural relics show a spatial distribution trend of north-north-east. Thirdly, that the urban functional zoning has been used to construct an urban relics protection area in which three suggestions for the protection of urban relics have been put forward: to establish a holistic protection system for urban planning functional areas; to establish a “district-axis-point” trinity protection model to promote the effective function recovery of urban planning and construction functional areas; and to continue the effective functions of the city. This provides reference for the study of modern urban planning ideas and solutions for current sustainable urban development, upgrading of public service facilities, and green low-carbon urban transformation.

The spatial pattern and dynamic evolution of basic public service level in Beijing-Tianjin-Hebei region from the perspective of COVID-19

PurposeThis study aims to investigate the basic public service level in the Beijing-Tianjin-Hebei region under the impact of COVID-19.Design/methodology/approachThis study constructed a basic public service-level evaluation system from the five dimensions of education, culture, health, social security and infrastructure and environment, and measures the basic public service level in 13 cities in Beijing, Tianjin and Hebei using the entropy method. The spatial pattern and dynamic evolution of the public service level are analysed from the perspective of dynamic trends in time series and spatial distribution, along with the reasons for the evolution of spatial distribution.Findings(1) The basic public service level in the 13 cities is generally on the rise, but the trend is unstable. (2) The basic public service level in space shows a general trend of attenuation from northeast to southwest, with significant spatial imbalance and orientation. (3) The regional differences first increase and then decrease. (4) The inter-group mobility of different basic public service levels is low, and cities with lower initial levels find it difficult to achieve leapfrog development. Moreover, the health service level of the region is still at a low stage, which is not conducive to effectively preventing and controlling the epidemic.Originality/valueFrom the perspective of this research, the spatial pattern and dynamic evolution of basic public service were adopted to analyse the coordinated development of the Beijing-Tianjin-Hebei region. Furthermore, this study discusses how to improve the basic public service level to ensure sustainable operation in the region under the impact of COVID-19.

Climatological Changes in Soil Moisture during the 21st Century over the Indian Region Using CMIP5 and Satellite Observations

Climate data records of soil moisture (SM) are fundamental for improving our understanding of long-term dynamics in the coupled water, energy, and carbon cycles over land. However, many of these studies rely on models for which the errors are not yet fully understood over a region. This may have a considerable impact on the economic growth of the country if the model’s future predictions are used for studying long-term trends. Here we examined the spatial distribution of past, present, and future predictions of SM studied using the Coupled Model Intercomparison Project Phase5 (CMIP5) simulations for the historical period (1850–2005) and future climate projections (2006–2099) based on Representative Concentration Pathways (RCP-RCP2.6, RCP4.5, RCP6.0, and RCP8.5). Furthermore, the performance of modeled SM with the satellite AMSR-E (Advanced Microwave Scanning Radiometer-Earth observation system) was studied. The modeled SM variations of 38 Global Climate Models (GCMs) show discreteness but still we observed that CESM1-CM5, CSIRO-MK3-6-0, BCC-CSM1-1, and also BCC-CSM1-1-M, NorESM1-M models performed better spatially as well as temporally in all future scenarios. However, from the spatial perspective, a large deviation was observed in the interior peninsula during the monsoon season from model to model. In addition, the spatial distribution of trends was highly diversified from model to model, while the Taylor diagram presents a clear view of the model’s performance with observations over the region. Skill score statistics also give the accuracy of model predictions in comparison with observations. The time series was estimated for the future trend of the SM along with the past few decades, whereas the preindustrial and industrial period changes were involved. Significant positive anomaly trends are noticed in the whole time series of SM during the future projection period of 2021–2099 using CMIP5 SM model datasets.

Spatial distribution, compositional pattern and source apportionment of n-alkanes in surface sediments of the Bohai Sea, Yellow Sea, and East China Sea and implications of carbon sink

China's marginal seas (CMSs, including the Bohai Sea, Yellow Sea, East China Sea) are a significant sink for both terrestrial organic matter (OM) and marine OM, and they play an important role in the global biogeochemical carbon cycle. The spatial distribution and origin of organic matter based on n-alkanes in the surface sediments of CMSs and the implications of carbon sinks were comparatively analyzed. The n-alkane content in surface sediment from the Bohai Sea was higher than that of the Yellow Sea and the East China Sea. The spatial distribution trends of marine and terrestrial organic matter are obviously different in the surface sediments of China's marginal seas. The n-alkanes in the sediments of the Yellow Sea and the East China Sea were mainly derived from terrestrial higher plants, and land-based influence gradually decreased from the near shore to the open sea. Higher concentration of terrigenous OM are concentrated nearshore, especially near estuaries, such as the Yellow River Estuary, the Old Yellow River Estuary and the Yangtze River Estuary. The input of n-alkanes from woody plants in the Bohai Sea area was slightly higher than that of herbaceous plants, and the input of herbaceous plants in the Yellow Sea and East China Sea was slightly dominant. The distribution of marine organic matter is controlled by marine productivity and the sedimentary environment. Due to climate change, the decomposition and enrichment of organic matter also show the climate effect of carbon molecular combinations. As a semiclosed sea area, the Bohai Sea was beneficial to the growth and reproduction of marine phytoplankton. From the perspective of petroleum pollution, the Bohai Sea was the most serious, followed by the Yellow Sea, and the East China Sea was the lightest. The carbon burial amount of terrestrial organic matter accounts for approximately 7% of the terrestrial organic matter burial amount of global marginal sea sediments, indicating that China's marginal sea plays an important role in the global carbon cycle. The result provide a basis for further understanding the source pattern and burial preservation of sedimentary organic matter in this sea area.

More tropical cyclones are striking coasts with major intensities at landfall

In this study, we show that the number of annual global tropical cyclone (TC) landfalls with major landfall intensity (LI ≥ 50 m s−1) has nearly doubled from 1982 to 2020. The lifetime maximum intensity (LMI) of global major landfalling TCs has been increasing by 0.8 m s−1 per decade (p < 0.05), but this significance of intensity change disappears at landfall (0.3 m s−1 per decade, p = 0.69). The lack of a significant LI trend is caused by the much larger variance of LI than that of LMI in all basins and explains why a significant count change of TCs with major intensity at landfall has only now emerged. Basin-wide TC trends of intensity and spatial distribution have been reported, but this long-term major TC landfall count change may be the most socio-economic significant.

The Impact of Climate Change on Urban Transportation Resilience to Compound Extreme Events

Global warming, sea-level rise, and rapid urbanization all increase the risk of compound extreme weather events, presenting challenges for the operation of urban-related infrastructure, including transportation infrastructure. In this context, some questions become important. For example, what are the temporal and spatial distribution and development trends of transportation resilience when considering the impact of multilpe extreme weather events on the urban transportation system? What is the degree of loss of urban transportation resilience (UT resilience) under different extreme event intensities, and how long will it take for the entire system to restore balance? In the future, if extreme weather events become more frequent and intense, what trends will urban transportation resilience show? Considering these problems, the current monitoring methods for transportation resilience under the influence of extreme events are lacking, especially the monitoring of the temporal and spatial dynamic changes of transportation resilience under the influence of compined extreme events. The development of big data mining technology and deep learning methods for spatiotemporal predictions made the construction of spatiotemporal data sets for evaluating and predicting UT resilience-intensity indicators possible. Such data sets reveal the temporal and spatial features and evolution of UT resilience intensity under the influence of compound extreme weather events, as well as the related future change trends. They indicate the key research areas that should be focused on, namely, the transportation resilience under climate warming. This work is especially important in planning efforts to adapt to climate change and rising sea levels and is relevant to policymakers, traffic managers, civil protection managers, and the general public.

Analyzing the Spatiotemporal Distribution and Characteristics of Liver Cirrhosis in Hospitalized Patients in Wuwei, Gansu Province During 1995-2016: A Long-Term Retrospective Study.

Objectives: This was a long-term retrospective study, aiming to understand the temporal and spatial trend of cirrhosis in Wuwei from 1995 to 2016, explore its spatio-temporal aggregation, and find out the high incidence areas. To provide theoretical basis for the formulation of comprehensive prevention and treatment strategy of cirrhosis in Wuwei. Methods: Herein, we extracted data of cirrhosis patients who were treated in 12 sentinel hospitals in Wuwei from their medical records. We used SAS and Joinpoint Regression Program for data analysis, SaTScan 9.4 software for clustering area detection, and ArcGIS 10.2 software for geographical distribution mapping. Results: Among 3308 patients with liver cirrhosis (average age, 55.34 years) included in this study, 15.9% were aged 50–54 years. The majority were men (2716, 65.8%), with a sex ratio of 1.92:1 and peasants by occupation (1369, 60.3%). The basic social medical insurance system covered the healthcare costs of 1271 patients (63%). A Joinpoint regression analysis done for 1995–2016 revealed an increase in the standardized cirrhosis rate [average annual percent change (AAPC) = 16.7% (95% CI, 10.2–23.5%)] with three joinpoints in 2010, 2013, and 2016. The annual percent change (APC) from 1995 to 2010 was 11.13% (95% CI: 6.5–16.0), and APC from 2010 to 2013 was 66.48% (95% CI:16.0–138.9); conversely, from 2013 to 2016, APC was 4.4% (95% CI, −7.5–17.8%). Hongshagang Town showed the highest average incidence. Each township showed a gradual increase in the incidence after 2010. The results revealed that in each township, liver cirrhosis incidence had some spatial aggregation and was nonrandom. Four liver cirrhosis clusters were noted in 75 townships in Wuwei. Data were gathered from 2011 to 2016. Conclusions: From 1995 to 2016, the incidence of cirrhosis in Wuwei still showed an increasing trend, but the growth rate slowed down since 2013. In Wuwei, the rate of standardization of cirrhosis in female patients increased steadily and faster than in male patients. It is necessary to strengthen the diagnosis, treatment, prevention, and control measures of cirrhosis-related diseases. The results of spatial scanning, basic spatial distribution, aggregation time, and time trend analysis were consistent.

Effect of the Type of Wind Data on Regional Potential Wind Erosion Estimation

The Agro-Pastoral Ecotone of Northern China (APEC) is a transitional area suffering from severe wind erosion. The wind data used in wind erosion modeling generally have different temporal resolutions and spatial station distributions. Previous studies have suggested that the temporal wind speed resolution influences the prediction of wind erosion events at the field scale. To date, no studies have been conducted to assess the impact of the type of wind data on regional wind erosion estimation. In this study, the Revised Wind Erosion Equation (RWEQ) and the Integrated Wind Erosion Modeling System (IWEMS) were used to evaluate the regional potential wind erosion in the Agro-Pastoral Ecotone of Northern China (APEC) during 2000 and 2012 based on four wind data type scenarios, including basic weather stations with daily wind statistics, basic weather stations with four wind speed measurements per day, reference climatological stations with daily wind statistics, and reference climatological stations with four wind speed measurements per day. The principal results reveal that the potential wind erosion estimates evaluated using the two models are closely correlated with the measured wind erosion data reported in the published literature, but the predicted values are generally lower than the observed values for the different scenarios. The magnitudes of the mean potential wind erosion ranged from 15.73 to 27.33 t ha−1 a−1 by RWEQ and changed between 61.77 and 98.54 t ha−1 a−1 by IWEMS for different scenarios. The spatial distribution and temporal trends of the annual or seasonal potential wind erosion obtained using the two models were similar for the different scenarios. This study revealed that wind speed is the most sensitive input, and hourly wind speed generated by the different temporal interpolation can significantly affect regional wind erosion estimation. Some studies involving precise regional wind erosion estimation, such as the impacts of landscape changes (land use/cover) on wind erosion, ecosystem service evaluation of reducing soil erosion, soil carbon sequestration and emissions through wind erosion, and wind erosion induced surface soil nutrient loss (e.g., nitrogen and phosphorus), may have been influenced by conducting regional wind erosion modeling based on different types of wind data. The users need to calibrate and validate the selected models for precise wind erosion prediction.

China's carbon budget inventory from 1997 to 2017 and its challenges to achieving carbon neutral strategies

The global climate change situation partly depends on the climate change policies of countries around the world, including China. Therefore, it is necessary to reasonably reduce carbon emissions (CEs) and increase carbon sink in order to progressively achieve the carbon neutral (CN) goal. However, the capacity and the potential to achieve carbon neutral with the support of its ecosystem, that is to say the carbon neutral capacity (CNC) and potential, are still unclear. To this end, based on China's energy emissions data, meteorological and hydrological data, lithology data, and vegetation data, we used the GEM-CO2 model, soil respiration model, spatial autocorrelation analysis method and other methods to establish the spatial information map of China city-scale CNC from 1997 to 2017. Furthermore, based on the future climate, vegetation data, CEs data and their influencing factors in 2025–2060, the Back Propagation neural network model was used to predict the CN potential of China's provinces. This study found during the study period, annual CEs of 5.63 Pg CO2 were not absorbed, which is about 90% of the average annual CEs. And the carbon surplus regions were mainly concentrated in the less developed northeastern and southwestern border regions. Moreover, the change in the CNC in China from 1997 to 2017 was −13.37 Tg/yr, indicating that the CNC of China's terrestrial ecosystems overall reduced. It should be noted that most provinces are not highly polarized, that is, there is no significant differences in CNC between cities in the provinces. Moreover, the scenario simulation's method of IPCC provides a reference for this manuscript, so we set up two future scenarios (A2 and B1 scenarios). In the future, China is expected to achieve a carbon emission peak before 2030 under the B1 scenario while it will continue to grow under the A2 scenario. From 2017 to 2060, the CNC under the two scenarios (A2/B1) will decrease by 44.58% and increase by 15.54%. It follows then that the road to CN in China will be difficult without corresponding policy intervention. In short, this study has clarified China's CNC from the past to the future, as well as CNC's spatial distribution and changing trends. This provided theoretical and data support for China to introduce corresponding zero-carbon solutions based on its understanding of CNC.

Spatio‐temporal rainfall variability and trend analysis in the Tekeze‐Atbara river basin, northwestern Ethiopia

AbstractSpatial and temporal rainfall variability analysis at a basin scale is essential to understand water availability for sustainable water resource management. This study investigated the spatial distribution and temporal rainfall trend in the Tekeze river basin using daily rainfall data collected from 43 meteorological stations with records from 1960 to 2019. Coefficient of variation, seasonality index (SI) and trend index were used to evaluate monthly, seasonal and annual rainfall variability in the basin. The non‐parametric Mann‐Kendall's test was used to understand any rainfall trends. Results showed that there is moderate annual rainfall variability in the basin. Rainfall variability is higher in the Tsedey, Bega and Belg than kiremt season. SI results showed that majority of the Tekeze river basin is categorized as seasonal, most of the rain occurring in less than 3 months. Overall, Mann‐Kendall's test result revealed that there is no significant annual rainfall trend in the basin except for four stations. The seasonal rainfall characteristics and short duration of rain in the basin indicate that there is a need to implement water harvesting measures during the wet season for use in the dry periods.

Spatial–Temporal Differentiation and the Driving Mechanism of Rural Transformation Development in the Yangtze River Economic Belt

Rural transformation development is an important part of rural revitalization, and the study of spatial–temporal differentiation characteristics and the driving mechanism of rural transformation development can provide a decision-making reference for formulating rural revitalization programs according to local conditions. Taking the Yangtze River Economic Belt as the research case, we constructed an evaluation system of rural transformation development in the dimension of “population, land and industry”, and the entropy TOPSIS method, spatial autocorrelation model and geographic detector model were used to reveal spatial-temporal differentiation and the driving mechanism of rural transformation development in the Yangtze River Economic Belt from 1978 to 2018. The results showed that (1) rural transformation development has been significantly unbalanced in the Yangtze River Economic Belt, and rural transformation development showed a weak distribution. Moreover, in terms of dimensions, the land use transformation level &gt; population development transformation level &gt; industrial development transformation level. (2) On the global, rural transformation development presented a spatial distribution trend of club convergence. On the local, the H(High)–H(High) type was mainly distributed in Shanghai City and the southern part of Jiangsu Province, and it evolved to Hangzhou-Shaoxing-Ningbo and Wanjiang City Belt; the L(Low)–L(Low) type was mainly concentrated in the western part of Sichuan Province and in the southwest of Yunnan Province. (3) Natural conditions, economic social development, traffic accessibility, policies and systems were the leading factors affecting rural transformation development in the Yangtze River Economic Belt. The influences of economic social development and traffic accessibility on rural transformation development were increasing, and policy factors had an important guiding effect on rural transformation development, and the influence of natural conditions on rural transformation development has weakened. This study can provide a practical reference for rural transformation and rural revitalization in the Yangtze River Economic Belt.

Spatial Prediction Method of Farmland Soil Organic Matter in Weibei Dryland of Shaanxi Province

Accurately predicting the spatial distribution of soil organic matter (SOM) content is of great significance for improving soil quality and improving the level of regional soil management. In order to explore the optimal model for predicting the SOM content of farmland in the Weibei Dryland of Shaanxi Province, the influence factors closely related to SOM content were selected as the modeling covariables, and a geographic detector, the ordinary kriging method (OK), geographic weighted regression model (GWR), partial least squares regression model (PLS), geographically weighted regression extended model (GWRPLS), and random forest model (RF) were used to predict the spatial distribution of SOM content in training samples. Additionally, the validation set samples were used to compare and analyze the prediction accuracy of the five methods. The results showed:① the main factors affecting the spatial variability of soil SOM were total nitrogen, fertilizer application, available potassium, available phosphorus, and altitude, and the interaction between any two factors was more explanatory for SOM than any single factor. ②ω(SOM) in farmland was between 2.25 and 30.23 g·kg-1, with an average value of 15.14 g·kg-1 and a coefficient of variation of 30.00. Although there were local differences in the prediction results of SOM by the five methods, the overall spatial distribution trend was basically the same. In the study area, the content of organic matter was low in the north and northeast and high in the west and southeast. ③ From the perspective of the prediction accuracy of the five methods, the root mean square error (RMSE) and mean absolute error (MAE) of RF were the smallest, and the prediction deviation (RPD) of GWRPLS was the largest. Compared with the OK method, the correlation coefficients (r) of GWR, PLS, RF, and GWRPLS increased to 0.907, 0.836, 0.968, and 0.972, respectively. Comprehensive analysis results showed that the random forest model had the highest prediction accuracy.

Wastewater surveillance of SARS-CoV-2 in Austria: development, implementation, and operation of the Tyrolean wastewater monitoring program.

Wastewater-based epidemiology (WBE) is an effective approach for tracking information on spatial distribution and temporal trends of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the community level. Herein, the development, implementation, and operation of the wastewater monitoring program serving Tyrol - a federal province of Austria - are described. The development of this program was initiated by Tyrolean health authorities at the end of the first phase of the Coronavirus disease 2019 (COVID-19) pandemic (May 2020). In close co-operation with the water sector and academic institutions, efficient and effective workflows and processes for wastewater surveillance were established. The monitoring program went into operation in November 2020. By the end of July 2021, a total of 5,270 wastewater influent samples collected at 43 sites were analyzed. The monitoring program provided valuable insights into the development of the pandemic situation in Tyrol and fulfilled several tasks that are of importance in different phases of the pandemic. It represented an early-warning system, provided independent confirmation of temporal trends in COVID-19 prevalence, enabled the assessment of the effectiveness of measures, alerted about bursts of disease activity, and provided evidence for the absence of COVID-19. These findings underline the importance of establishing national wastewater monitoring programs as a complementary source of information for efficient and effective pandemic management.

Climatological Characteristics and Aerosol Loading Trends from 2001 to 2020 Based on MODIS MAIAC Data for Tianjin, North China Plain

The North China Plain (NCP) in East Asia has a severe air pollution problem. In this study, the long-term spatial distribution and interannual trends of aerosol optical depth (AOD) were investigated using the MODIS MAIAC (multiangle implementation of the atmospheric correction) dataset from 2001 to 2020 for Tianjin, a city on the NCP. The annual AOD in Tianjin was 0.59 from 2001 to 2020. The average AOD of Tianjin was the highest in summer (0.96), followed by spring (0.58) and autumn (0.51). The annual AOD in Tianjin increased significantly in 2008 (approximately 0.77), and the minimum annual AOD was observed in 2020 (0.41). In summer, AOD in the 11 districts of Tianjin significantly increased from 2001 to 2010 and gradually decreased from 2011 to 2020. The occurrence frequency of AOD in the range of 0.2–0.5 was high in Tianjin accounting for almost 40% of the total proportion. In Tianjin, AOD exhibited a positive trend from 2001 to 2008 and an obvious negative growth trend from 2009 to 2020 due to anthropogenic emission. The findings are valuable for analyzing the climatological characteristics of aerosol loading and their optical properties at the district level of cities on the NCP.

Estimation of maize straw production and appropriate straw return rate in China

Accurate estimation of maize (Zea mays L.) straw production and exploration of appropriate straw return rate have important reference for guiding the comprehensive utilization of crop straw. In this study, we estimated the maize straw production (including aboveground straw and root straw) in recent ten years (2009–2018) of the four largest maize growing regions across China using the latest obtained region-specific maize straw index and root-shoot ratio and then determined the appropriate straw return rate of different regions based on the carbon (C) balance between straw C input and the total C emissions generated in maize cultivation. Our results showed that there were significant differences in maize straw index (MSI) and root-shoot ratio between different maize growing regions in China with spatial distribution trends of Huanghuaihai (HM) > North (NM) > Southwest (SW) > Northwest (NW) maize regions and SW > NW > NM > HM, respectively. On average, in the recent ten years, the total maize straw production of the four major regions was 216.03 Mt yr−1, including 192.73 Mt yr−1 for the aboveground straw production and 23.30 Mt yr−1 for root straw production. Nationally, when the maize straw return rate was 70.4% (45.8%, 57.4%, 62.2% and 84.5% in SW, HM, NW and NM respectively), the straw C input into soil can theoretically counterbalance the total C emission generated in maize cultivation. Additionally, the total soil organic C sequestration increased from 11.26 to 18.88 Mt C yr−1 for the whole four regions under the scenarios of 0%, 25%, 50%, 75% and 100% maize straw return.

Local fishers experience can contribute to a better knowledge of marine resources in the Western Mediterranean Sea

In this study we explore fishers’ perceptions in the Ebro Delta area, Western Mediterranean, throughout structured questionnaires. Specifically, we compare the coherence of information provided by fishers with those resulting by scientific studies in terms of the status of demersal marine resources in the Ebro Delta area; we describe temporal trends of demersal key species in the area with fishers’ perceptions; and we explore the use of Local Ecological Knowledge (LEK) as an alternative data sources to reduce the gap of knowledge on the spatial distribution and temporal trends of sensitive and vulnerable benthic marine species. Our results document important changes in the bottom trawling activity in the study area and in the abundance of commercial species with time according to fishers’ perceptions. Our results mostly match quantitative information from national statistics and stock assessment analyses. In addition, we document important changes in the presence of Vulnerable Marine Ecosystems (VMEs) in the study area due to the by-catch of habitat forming species with time. According to fishers’ perceptions, habitat forming species occur in the area, but were more frequently captured incidentally in the 1980 s and 1990 s. Overall, our study evidences that fisher’s perception represents an important source of complementary information to describe bottom trawling fisheries characteristics and the long-term changes of demersal target species in the Western Mediterranean Sea. Moreover, it highlights that LEK can provide fundamental and additional information on ecosystem changes and it can be a useful tool to increase the knowledge on spatial distribution and on temporal trends of vulnerable benthic species.