Study on the spatial and temporal evolutionary characteristics of ozone concentration and the impact of human activities in China in 2019

Ozone(O_(3))pollution has a profound impact on human health,vegetation development,and the ecological environment,making it a critical focus of global academic research.In recent years,O_(3)pollution in China has been on a steady rise,with ozone emerging as the sole conventional pollutant to consistently increase in concentration without any decline.This study conducted a quantitative analysis of O_(3)concentrations across 367 Chinese cities in 2019,examining spatial autocorrelation and local clustering of O_(3)levels,and investigated the diverse relationships between human activity factors and O_(3)concentration.The seasonal fluctuation of O_(3)exhibited the“M-type”pattern,with peak concentrations in winter and the lowest levels in summer.The center of O_(3)pollution migrated southeastward,with the area of highest concentration progressively shifting south along the eastern coast.Moreover,O_(3)concentration showed a strong positive correlation with population density,road freight volume,and industrial emissions,suggesting that human activities,vehicle emissions,and industrial operations are significant contributors to O_(3)production.The results provide comprehensive information on the characteristics,causes,and occurrence mechanism of O_(3)in Chinese cities that can be utilized by global government departments to formulate strategies to prevent and control O_(3)pollution.

Spatial Variation in CO_(2) Concentration Improves the Simulated Surface Air Temperature Increase in the Northern Hemisphere

The increasing concentration of atmospheric CO_(2) since the Industrial Revolution has affected surface air temperature.However,the impact of the spatial distribution of atmospheric CO_(2) concentration on surface air temperature biases remains highly unclear.By incorporating the spatial distribution of satellite-derived atmospheric CO_(2) concentration in the Beijing Normal University Earth System Model,this study investigated the increase in surface air temperature since the Industrial Revolution in the Northern Hemisphere(NH) under historical conditions from 1976-2005.In comparison with the increase in surface temperature simulated using a uniform distribution of CO_(2),simulation with a nonuniform distribution of CO_(2)produced better agreement with the Climatic Research Unit(CRU) data in the NH under the historical condition relative to the baseline over the period 1901-30.Hemispheric June-July-August(JJA) surface air temperature increased by 1.28℃ ±0.29℃ in simulations with a uniform distribution of CO_(2),by 1.00℃±0.24℃ in simulations with a non-uniform distribution of CO_(2),and by 0.24℃ in the CRU data.The decrease in downward shortwave radiation in the non-uniform CO_(2) simulation was primarily attributable to reduced warming in Eurasia,combined with feedbacks resulting from increased leaf area index(LAI) and latent heat fluxes.These effects were more pronounced in the non-uniform CO_(2)simulation compared to the uniform CO_(2) simulation.Results indicate that consideration of the spatial distribution of CO_(2)concentration can reduce the overestimated increase in surface air temperature simulated by Earth system models.

Spatial Inhomogeneity of Atmospheric CO_(2) Concentration and Its Uncertainty in CMIP6 Earth System Models

This paper provides a systematic evaluation of the ability of 12 Earth System Models(ESMs)participating in the Coupled Model Intercomparison Project Phase 6(CMIP6)to simulate the spatial inhomogeneity of the atmospheric carbon dioxide(CO_(2))concentration.The multi-model ensemble mean(MME)can reasonably simulate the increasing trend of CO_(2) concentration from 1850 to 2014,compared with the observation data from the Scripps CO_(2) Program and CMIP6 prescribed data,and improves upon the CMIP5 MME CO_(2) concentration(which is overestimated after 1950).The growth rate of CO_(2) concentration in the northern hemisphere(NH)is higher than that in the southern hemisphere(SH),with the highest growth rate in the mid-latitudes of the NH.The MME can also reasonably simulate the seasonal amplitude of CO_(2) concentration,which is larger in the NH than in the SH and grows in amplitude after the 1950s(especially in the NH).Although the results of the MME are reasonable,there is a large spread among ESMs,and the difference between the ESMs increases with time.The MME results show that regions with relatively large CO_(2) concentrations(such as northern Russia,eastern China,Southeast Asia,the eastern United States,northern South America,and southern Africa)have greater seasonal variability and also exhibit a larger inter-model spread.Compared with CMIP5,the CMIP6 MME simulates an average spatial distribution of CO_(2) concentration that is much closer to the site observations,but the CMIP6-inter-model spread is larger.The inter-model differences of the annual means and seasonal cycles of atmospheric CO_(2) concentration are both attributed to the differences in natural sources and sinks of CO_(2) between the simulations.

Spatiotemporal Differentiation of Urban Spatial Form and Carbon Emissions in Poyang Lake City Group

In response to the inherent requirements of low-carbon land spatial planning in Jiangxi Province and the lack of existing research,this paper explored the mechanism of spatial form elements of Poyang Lake urban agglomeration on urban carbon emissions.Based on generalized linear regression and geographically weighted regression models,this paper analyzed the spatiotemporal distribution characteristics of carbon emissions,the spatiotemporal relationship between urban form index and carbon emissions,and the spatial differentiation of the intensity of dominant factors from 63 county-level administrative units in the Poyang Lake city group from 2005 to 2020.The results showed that:①The carbon emissions of urban agglomerations around Poyang Lake are generally increasing,and the spatial distribution of carbon emissions is characterized by high-value concentration in the middle and low-value agglomeration in pieces;②The main driving factor for the spatial heterogeneity of carbon emissions was the expansion of built-up area;③Improving urban compactness and optimizing urban form could effectively reduce urban carbon emissions.The results showcased the correlation between urban spatial landscape pattern and the spatiotemporal distribution of carbon emissions,which could make the low-carbon land spatial planning in the Poyang Lake city group more reasonable and practical.

Temporal and spatial changes of suspended sediment concentration and resuspension in the Yangtze River estuary

A detailed analysis of suspended sediment concentration (SSC) variations over a year period is presented using the data from 8 stations in the Yangtze River estuary and its adjacent waters, together with a discussion of the hydrodynamic regimes of the estuary. Spatially, the SSC from Xuliujing downwards to Hangzhou Bay increases almost constantly, and the suspended sediment in the inner estuary shows higher concentration in summer than in winter, while in the outer estuary it shows higher concentration in winter than in summer, and the magnitude is greater in the outer estuary than in the inner estuary, greater in the Hangzhou Bay than in the Yangtze River estuary. The sediments discharged by the Yangtze River into the sea are resuspended by marine dynamics included tidal currents and wind waves. Temporally, the SSC shows a pronounced neap-spring tidal cycle and seasonal variations. Furthermore, through the analysis of dynamic mechanism, it is concluded that wave and tidal current are two predominant factors of sediment resuspension and control the distribution and changes of SSC, in which tidal currents control neap-spring tidal cycles, and wind waves control seasonal variations. The ratio between river discharge and marine dynamics controls spatial distribution of SSC.

HQ-Tree： A Distributed Spatial Index Based on Hadoop

In this paper,we propose a novel spatial data index based on Hadoop:HQ-Tree.In HQ-Tree,we use PR QuadTrec to solve the problem of poor efficiency in parallel processing,which is caused by data insertion order and space overlapping.For the problem that HDFS cannot support random write,we propose an updating mechanism,called "Copy Write",to support the index update.Additionally,HQ-Tree employs a two-level index caching mechanism to reduce the cost of network transferring and I/O operations.Finally,we develop MapReduce-based algorithms,which are able to significantly enhance the efficiency of index creation and query.Experimental results demonstrate the effectiveness of our methods.

ANALYSIS OF HABITAT PATTERN CHANGE OF RED-CROWNED CRANES IN THE LIAO HE DELTA USING SPATIAL DIVERSITY INDEX

Habitat pattern change of red-crowned cranes (Grus japonensis) in t he Liaohe Delta between 1988 and 1998 was analyzed with the help of Spatial Dive rsity Index based on remote sensing data and field investigation. The result sho wed that the influence from human activities on the wetland habitat of red-crow ned cranes was prominent with the development of oil and agricultural exploitati on, and the habitat pattern of red-crowned cranes had been obviously changed by the human disturbance during the ten years. The areas with high Spatial Diversi ty values (SD≥0.65) and that with mid-high values (0.5≤SD< 0.65), which const ituted the main part of suitable habitat of red-crowned cranes,had reduced to 9142ha and 5576ha respectively, with the shrinking of natural land cover, such a s reed and Suaeda community. The habitat pattern became more fragmented, which w as caused by roads and wells during oil exploration. It was indicated that the s uitability and quality of habitat for red-crowned cranes in the Liaohe Delta we re degraded in the last decade. The results also showed that diversity index cou ld reflect the habitat suitability of red-crowned cranes quantitatively and des cribe the spatial pattern of the habitat explicitly. This study will provide a s cientific basis for habitat protection of red-crowned cranes and other rare spe cies in wetlands.

2-Level R-tree Index Based on Spatial Grids and Hilbert R-tree

Multi-level spatial index techniques are always used in large spatial databases. After a general survey of R-tree relevant techniques, this paper presents a novel 2-level index structure, which is based on the schemas of spatial grids, Hilbert R-tree and common R-tree. This structure is named H2R-tree, and it is specifically suitable for the indexing highly skewed, distributed, and large spatial database. Algorithms and a sample are given subsequently.

Research on Spatial Changes of Negative Air Ion Concentration in Hefei City

[Objective] The study aimed to discuss the spatial changes of negative air ion concentration in Hefei City. [Method] Based on the observation of air ions, temperature, relative humidity, the spatial changes of negative air ion concentration in different districts of Hefei City were ana- lyzed firstly, then the correlation between negative air ion concentration and meteorological factors was discussed. [ Result] Air cleanliness index （CI） of the parks, residential areas, industrial regions, transport stations and prosperous commercial districts was 0.86, 0.53, 0.37, 0.26 and 0.17 respectively, and the latter two regions suffered mild and moderate pollution separately. Daily variations of negative air ion concentration in the residential areas and parks of Hefei City were obvious, showing U shape, that is, the maximum values appeared in the morning and evening, while the minimum values could be found around 14 ：（30. There was no distinctly regular variation of negative air ion concentration in the prosperous com- mercial districts, transport stations and industrial areas. In Hefei City, the concentration of negative air ions showed an increasing trend from the ur- ban districts to the suburban districts; it was obviously higher in the residential areas and parks with numerous plants and waters compared with the prosperous commercial districts and transport stations. Negative air ion concentration correlated with relative humidity positively and temperature negatively, so the main meteorological factors influencing the negative air ion concentration in Hefei City were temperature and relative humidity. [ Coedusloa] The research could provide scientific references for city planning and greenbelt construction in future.

A Novel Hybrid Method for Measuring the Spatial Autocorrelation of Vehicular Crashes: Combining Moran’s Index and Getis-Ord G_i<sup style='margin-left:-7px;'>*</sup>Statistic

Spatial autocorrelation is a measure of the correlation of an observation with other observations through space. Most statistical analyses are based on the assumption that the values of observations are independent of one another. Spatial autocorrelation violates this assumption, because observations at near-by locations are related to each other, and hence, the consideration of spatial autocorrelations has been gaining attention in crash data modeling in recent years, and research have shown that ignoring this factor may lead to a biased estimation of the modeling parameters. This paper examines two spatial autocorrelation indices: Moran’s Index;and Getis-Ord Gi* statistic to measure the spatial autocorrelation of vehicle crashes occurred in Boone County roads in the state of Missouri, USA for the years 2013-2015. Since each index can identify different clustering patterns of crashes, therefore this paper introduces a new hybrid method to identify the crash clustering patterns by combining both Moran’s Index and Gi*?statistic. Results show that the new method can effectively improve the number, extent, and type of crash clustering along roadways.

The effects of PM_(2.5)concentration on residents'health from the perspective of spatial economics

Ambient air quality is an important part of the ecological environment.Based on panel data from 192 countries for the period 2010–2016,our study applies spatial geography elements in a spatial panel model to analyze whether PM_(2.5)harms residents'health.We find a positive correlation between PM_(2.5)concentration and the prevalence of tuberculosis.Empirical testing shows that if residents live in environments with high PM_(2.5)concentrations for an extended period,it increases their probability of contracting tuberculosis.PM_(2.5)concentration and economic growth have an environmental Kuznets curve(EKC)relationship.Furthermore,PM_(2.5)concentration and prevalence of tuberculosis in different countries have a positive spatial correlation during the study period.The values of PM_(2.5)concentration in adjacent areas are similar,because PM_(2.5)can cross borders through airflow and as economic development levels in adjacent regions are similar.When regulating haze pollution,we should adopt regional joint governance,consider the specific characteristics of different regions,and coordinate these regulations with environmental protection policies to realize the goal of“lucid waters and lush mountains.”

A Methodology for Estimating Leaf Area Index by Assimilating Remote Sensing Data into Crop Model Based on Temporal and Spatial Knowledge

In this paper,a methodology for Leaf Area Index(LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge.Firstly,sensitive parameters of crop model were calibrated by Shuffled Complex Evolution method developed at the University of Arizona(SCE-UA) optimization method based on phenological information,which is called temporal knowledge.The calibrated crop model will be used as the forecast operator.Then,the Taylor′s mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer(MODIS) multi-scale data,which was used to calibrate the LAI inversion results by A two-layer Canopy Reflectance Model(ACRM) model.The calibrated LAI result was used as the observation operator.Finally,an Ensemble Kalman Filter(EnKF) was used to assimilate MODIS data into crop model.The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products.The root mean square error(RMSE) of LAI calculated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation(0.3795),and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265.All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI.

Prediction method for risks of coal and gas outbursts based on spatial chaos theory using gas desorption index of drill cuttings

Based on the evolution of geological dynamics and spatial chaos theory, we proposed the advanced prediction an advanced prediction method of a gas desorption index of drill cuttings to predict coal and gas outbursts. We investigated and verified the prediction method by a spatial series data of a gas desorption index of drill cuttings obtained from the 113112 coal roadway at the Shitai Mine. Our experimental results show that the spatial distribution of the gas desorption index of drill cuttings has some chaotic charac- teristics, which implies that the risk of coal and gas outbursts can be predicted by spatial chaos theory. We also found that a proper amount of sample data needs to be chosen in order to ensure the accuracy and practical maneuverability of prediction. The relative prediction error is small when the prediction pace is chosen carefully. In our experiments, it turned out that the optimum number of sample points is 80 and the optimum prediction pace 30. The corresponding advanced prediction pace basically meets the requirements of engineering applications.

Spatial-temporal Analysis of Daily Air Quality Index in the Yangtze River Delta Region of China During 2014 and 2016

Urban air pollution is a prominent problem related to the urban development in China, especially in the densely populated urban agglomerations. Therefore, scientific examination of regional variation of air quality and its dominant factors is of great importance to regional environmental management. In contrast to traditional air pollution researches which only concentrate on a single year or a single pollutant, this paper analyses spatiotemporal patterns and determinants of air quality in disparate regions based on the air quality index(AQI) of the Yangtze River Delta region(YRD) of China from 2014 to 2016. Results show that the annual average value of the AQI in the YRD region decreases from 2014 to 2016 and exhibit a basic characteristic of ‘higher in winter, lower in summer and slightly high in spring and autumn'. The attainment rate of the AQI shows an apparently spatial stratified heterogeneity, Hefei metropolitan area and Nanjing metropolitan area keeping the worst air quality. The frequency of air pollution occurring in large regions was gradually decreasing during the study period. Drawing from entropy method analysis, industrialization and urbanization represented by per capita GDP and total energy consumption were the most important factors. Furthermore, population agglomeration is a factor that cannot be ignored especially in some mega-cities. Limited to data collection, more research is needed to gain insight into the spatiotemporal pattern and influence mechanism in the future.

Detection of Multi-dimensional Driving Forces of Public Environmental Concern in China:Based on Spatial Heterogeneity Perspectives

Public environmental concern(PEC)is an important bottom-up force in building an environmentally sustainable society.Guided by attitude theory,this paper innovatively constructed a PEC evaluation index system,while introducing entropy weighted-TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)to realize the assessment of PEC.Exploratory spatial data analysis was used to portray the spatio-temporal evolution patterns of PEC in 362 Chinese cities at prefecture-level and above from 2011 to 2018.Furthermore,the Geodetector model was performed to identify the multi-dimensional determinants of PEC from the perspective of spatial heterogeneity.The results indicated that:1)PEC in China exhibited a fluctuating upward trend,consistent with the spatial distribution law of‘Heihe-Tengchong Line’and‘Bole-Taipei Line’;2)the driving effect of each factor varied dynamically,but in general,economic development level,population size,industrial wastewater,and education level were the dominant driving factors explaining the spatial variation of PEC;3)risk detection revealed that four factors,government environmental regulations,PM_(2.5),vegetation coverage,and natural resource endowment,had nonlinear effects on PEC;4)the interactions between factors all demonstrated an enhancement in explaining the spatial differentiation of PEC.PEC was driven by the comprehensive interaction of four-dimensional factors of economy,society,pollutant emissions,and ecology.Among them,population agglomeration accompanied by a high level of regional economy and information technology can explain the increase in PEC to the greatest extent.

Integration of Geological, Geophysical and Seismological Data for Seismic Hazard Assessment Using Spatial Matching Index

Probabilistic seismic hazard assessment (PSHA) takes into account as much data as possible for defining the initial seismic source zone model. In response to this, an algorithm has been developed for integration of geological, geophysical and seismological data through a spatial index showing the presence or absence of a potential seismic source feature in the input data. The spatial matching index (SMI) is calculated to define the coincidence of independent data showing any indications for existence of a fault structure. It is applied for hazard assessment of Bulgaria through quantification of the seismic potential of 416 square blocks, 20 × 20 km in size covering the entire territory of Bulgaria and extended by 20 km outside of the country borders. All operations are carried out in GIS environment using its capabilities to work with different types of georeferenced spatial data. Results show that the highest seismic potential (largest SMI) is observed in 56 block elements (13% of the territory) clearly delineating cores of the source zones. Partial match is registered in 98 block elements when one of the features is missing. Not any evidence for earthquake occurrence is predicted by our calculation in 117 elements, comprising 28% of the examined area. The quantitative parameter for spatial data integration which is obtained in the present research may be used to analyze information regardless of its type and purpose.

The Spatial Distribution of Nitrite Concentrations in a Large Drinking Water Distribution System in Finland

Nitrite in drinking water is a potential health hazard and monitoring its concentrations in distributed water is of paramount importance. When monochloramine is used in secondary disinfection in drinking water distribution systems (DWDSs), nitrite is often formed by nitrification in the biofilm on the inner surface of distribution pipes. This article attempts to identify areas with a risk of increased nitrite concentrations as well as the main reasons leading to nitrite occurrence in a large urban DWDS in Finland using spatial inspection of obligatory monitoring data. Nitrification was found to occur throughout the study area, though nitrite was not increased everywhere. Instead, nitrite was increased close to the water treatment plants (WTPs) and was connected to fresh drinking water than stagnant drinking water. Temperature effects on nitrite concentrations were surprisingly insignificant, even though it is well known that nitrification reactions are affected by temperature. The temperature dependence of ammonium and total residual chlorine was more significant than the dependence of nitrite. The findings of this study emphasize the need to monitor nitrite concentrations close to WTPs.

Model-Driven Deep Learning for Massive Space-Domain Index Modulation MIMO Detection

In this paper,a powerful model-driven deep learning framework is exploited to overcome the challenge of multi-domain signal detection in spacedomain index modulation(SDIM)based multiple input multiple output(MIMO)systems.Specifically,we use orthogonal approximate message passing(OAMP)technique to develop OAMPNet,which is a novel signal recovery mechanism in the field of compressed sensing that effectively uses the sparse property from the training SDIM samples.For OAMPNet,the prior probability of the transmit signal has a significant impact on the obtainable performance.For this reason,in our design,we first derive the prior probability of transmitting signals on each antenna for SDIMMIMO systems,which is different from the conventional massive MIMO systems.Then,for massive MIMO scenarios,we propose two novel algorithms to avoid pre-storing all active antenna combinations,thus considerably improving the memory efficiency and reducing the related overhead.Our simulation results show that the proposed framework outperforms the conventional optimization-driven based detection algorithms and has strong robustness under different antenna scales.

Effects of leaf N concentration and leaf area index on determining rice tillering

Relative tillering rate(RTR)increased linear-ly with the increasing of leaf N concentration(NLV)has been already reported.To testwhether this relationship could be used toquantitatively explain the difference in tilleringamong a wide range of N application,field ex- periments were conducted at the IRRI farm,Los Banos,Laguna,the Philippines.Two in- dica cultivars,IR 72 and IR68284H wereused.For each cultivar,12 treatments includ- ing 4 N levels(0,60,120,and 180kgN·ha)and 3 transplanting spacing(30×20,20×20,and 10×20cm)were arranged in a ran-domized split-plot design with 4 replications.The N treatments were designated as mainplots and spacings as subplots.Fourteen-day-old seedlings were transplanted with 3seedlings per hill.The subplot area was 20m~2.Nitrogen fertilizer was applied as basal,atmidtillering,and at panicle initiation in threeequal splits.P,K,and Zn were applied asbasal at normal dosage.The field was flooded.Plant samples were taken every 7-14 d from 14d after transplanting to flower

一种基于迈克尔逊干涉仪的浓度/折射率在线测量系统的研究

浓度是表征溶液性质的重要物理参数之一。本文设计了一种基于旋转比色皿结合迈克尔逊干涉仪的浓度/折射率在线测量系统。将液体加入比色皿并旋转5°,利用CCD采集光强数据和自编的计数程序,可实时测量旋转前后干涉条纹移动数目之差,并由此计算NaCl和葡萄糖溶液的折射率和浓度。对质量百分比浓度为0、10%和20%的NaCl溶液进行了折射率测量和误差分析,发现与相关理论值的相对误差仅为0.61%、0.63%和2.2%。葡萄糖溶液的浓度—折射率关系曲线也呈现出线性良好、可靠性高的特点。总之,基于迈克尔逊干涉仪的浓度/折射率在线测量系统具有测量非接触、结果可靠、高效实用的特点。