Influence Of Spatial Heterogeneity Research Articles

Characterizing the Effect of Spatial Heterogeneity and the Deployment of Sampled Plots on the Uncertainty of Ground “Truth” on a Coarse Grid Scale: Case Study for Near‐Infrared (NIR) Surface Reflectance

AbstractThe quality of surface reflectance product is fundamentally important to ensure the information extracted from the downstream products can be trusted. However, ground validation of surface reflectance satellite products is challenging, because ground “truth” on a coarse grid scale based on sparse ground measurements is subject to uncertainty due to spatial heterogeneity. Based on a high‐resolution numerical simulation, this study quantifies the influence of spatial heterogeneity on the uncertainty of surface reflectance ground “truth” in different sampling cases (i.e., the number and positions of sampled plots). The effect of spatial heterogeneity is very small and even negligible when optimal locations are identified. By contrast, for the averaged condition without optimal sampling, spatial heterogeneity is almost the only source of uncertainty of ground “truth,” with determination coefficients of 0.94, 0.99, and 1 for 1, 2, and 3 sampled plots, respectively. The response coefficients between them are about 0.77, 0.55, and 0.47 for 1, 2, and 3 sampled plots, respectively. Optimal sampling plays a more important role in reducing the uncertainty of ground “truth” than increasing the number of sampled plots. Particularly, the latter is not helpful in reducing the uncertainty when the worst locations are adopted. Furthermore, the thresholds of spatial heterogeneity of surface reflectance to meet a predefined uncertainty of 0.01 (about 3.5% for surface reflectance over the study area) were given for different sampling cases over northern China. This study provides important guidance to aid in situ network design for the validation of 1 km satellite surface reflectance products.

The sustainability of industrial structure on green eco-efficiency in the Yellow River Basin

China attaches great importance to the green eco-efficiency and sustainability of the Yellow River Basin (YRB), but the level and influencing factors of such eco-efficiency there lack in-depth analysis. Thus, this research uses the Slack Based Model (SBM) to calculate the green eco-efficiency of YRB, uses the overall feasible generalized least square method (FGLS) to estimate the regression model, and employs data of 43 cities from 2007 to 2018 to analyze the sustainability of industrial structure upgrading. The results are as follows. First, the overall level of YRB’s green eco-efficiency is low. Second, spatial distribution presents the heterogeneity characteristics of the “middle reaches a depression”, displaying a fluctuating trend after the first decline and then a rise together with changes in time. Third, due to the influence of spatial heterogeneity, the levels of sustainability of industrial structure optimization on green eco-efficiency of the reaches of YRB vary.

Machine learning approach for spatial modeling of ridesourcing demand

Accurately forecasting ridesourcing demand is important for effective transportation planning and policy-making. With the rise of Artificial Intelligence (AI), researchers have started to utilize machine learning models to forecast travel demand, which, in many cases, can produce higher prediction accuracy than statistical models. However, most existing machine-learning studies used a global model to predict the demand and ignored the influence of spatial heterogeneity (i.e., the spatial variations in the impacts of explanatory variables). Spatial heterogeneity can drive the parameter estimations varying over space; failing to consider the spatial variations may limit the model's prediction performance. To account for spatial heterogeneity, this study proposes a Clustering-aided Ensemble Method (CEM) to forecast the zone-to-zone (census-tract-to-census-tract) travel demand for ridesourcing services. Specifically, we develop an interactive clustering approach (powered by human-in-the-loop AI) to split the origin-destination pairs into different clusters and ensemble the cluster-specific machine learning models for prediction. We implement and test the proposed methodology by using the ridesourcing-trip data in Chicago. The results show that, with a more transparent and flexible model structure, the CEM significantly improves the prediction accuracy than the benchmark models (i.e., global machine-learning and statistical models directly trained on all observations). This study offers transportation researchers and practitioners a new methodology of travel demand forecasting, especially for new travel modes like ridesourcing and micromobility.

Effects of anisotropic diffusion on Turing patterns in heterogeneous environment

Diffusion plays a crucial role in the forming and evolving of Turing patterns. Generally, the diffusion processes in complex systems do not comply to the complete random walk theory, which means that the diffusion is abnormal rather than normal, such as super-diffusion, sub-diffusion and anisotropic diffusion. However, most of previous studies focused on the pattern formation mechanism under the normal diffusion. In this paper, a two-component reaction-diffusion model with anisotropic diffusion is used to study the effect of anisotropic diffusion on Turing patterns in heterogeneous environments. Three different types of anisotropic diffusions are utilized. It is shown that the system gives rise to stripe patterns when the degree of anisotropic diffusion is high. The directions of stripes are determined by the degree of the diffusion coefficient deviating from the bifurcation point. In a low degree of anisotropic diffusion, the pattern type is the same as the counterpart in a low degree of the isotropic diffusion. When the diffusion coefficient grows linearly in the space, different types of patterns compete with each other and survive in different regions under the influence of spatial heterogeneity. When the diffusion coefficient is modulated by a one-dimensional periodic function, both type and wavelength of the pattern are determined by the modulated wavelength and the intrinsic wavelength. The system can exhibit alternating two-scale mixed patterns of different types when the modulated wavelength is larger than the intrinsic wavelength. Note that each of the diffusion coefficients of some special anisotropic media is a tensor, which can be expressed as a matrix in two-dimensional cases. We also study the influence of off-diagonal diffusion coefficient <i>D</i> on Turing pattern. It is found that the Turing pattern induced by off-diagonal diffusion coefficient always selects the oblique stripe pattern. The off-diagonal diffusion coefficient <i>D</i> not only affects the pattern selection mechanism, but also expands the parameter range of Turing space. The critical diffusion coefficient <inline-formula><tex-math id="M3">\begin{document}$ {D_{\text{c}}} $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="24-20221294_M3.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="24-20221294_M3.png"/></alternatives></inline-formula> increases linearly with the diagonal diffusion coefficient <inline-formula><tex-math id="M4">\begin{document}$ {D_u} $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="24-20221294_M4.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="24-20221294_M4.png"/></alternatives></inline-formula> increasing. The intrinsic wavelength of the oblique stripe pattern decreases as the off-diagonal diffusion coefficient <i>D</i> increases. It is interesting to note that the critical wavelength corresponding to the critical diffusion coefficient <inline-formula><tex-math id="M6">\begin{document}$ {D_{\text{c}}} $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="24-20221294_M6.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="24-20221294_M6.png"/></alternatives></inline-formula> is independent of the diagonal diffusion coefficient <inline-formula><tex-math id="M7">\begin{document}$ {D_u} $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="24-20221294_M7.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="24-20221294_M7.png"/></alternatives></inline-formula>. These results not only provide a new insight into the formation mechanism of Turing patterns, but also increase the range and complexity of possible patterns.

Heterogeneity around CO2 vents obscures the effects of ocean acidification on shallow reef communities

AbstractStudies that use CO2 vents as natural laboratories to investigate the impacts of ocean acidification (OA) typically employ control-impact designs or local-scale gradients in pH or pCO2, where impacted sites are compared to reference sites. While these strategies can accurately represent well-defined and stable vent systems in relatively homogenous environments, it may not adequately encompass the natural variability of heterogeneous coastal environments where many CO2 vents exist. Here, we assess the influence of spatial heterogeneity on the perceived impacts of OA at a vent system well established in the OA literature. Specifically, we use a multi-scale approach to investigate and map the spatial variability in seawater pH and benthic communities surrounding vents at Whakaari-White Island, New Zealand to better understand the scale and complexity of ecological impacts of an acidified environment. We found a network of vents embedded in complex topography throughout the study area, and spatially variable pH and pCO2 levels. The distribution of habitats (i.e. macroalgal forests and turfing algae) was most strongly related to substratum type and sea urchin densities, rather than pH. Epifaunal communities within turfing algae differed with sampling distance from vents, but this pattern was driven by higher abundances of a number of taxa immediately adjacent to vents, where pH and temperature gradients are steep and white bacterial mats are prevalent. Our results contrast with previous studies at White Island that have used a control-impact design and suggested significant impacts of elevated CO2 on benthic communities. Instead, we demonstrate a highly heterogeneous reef where it is difficult to separate effects of reduced pH from spatial variation in reef communities. We urge that future research carefully considers and quantifies the biological and physical complexity of venting environments, and suggest that in dynamic systems, such as White Island, the use of control-impact designs can oversimplify and potentially overestimate the future impacts of OA.

Remote Sensing Estimation of Bamboo Forest Aboveground Biomass Based on Geographically Weighted Regression

Bamboo forests are widespread in subtropical areas and are well known for their rapid growth and great carbon sequestration ability. To recognize the potential roles and functions of bamboo forests in regional ecosystems, forest aboveground biomass (AGB)—which is closely related to forest productivity, the forest carbon cycle, and, in particular, carbon sinks in forest ecosystems—is calculated and applied as an indicator. Among the existing studies considering AGB estimation, linear or nonlinear regression models are the most frequently used; however, these methods do not take the influence of spatial heterogeneity into consideration. A geographically weighted regression (GWR) model, as a spatial local model, can solve this problem to a certain extent. Based on Landsat 8 OLI images, we use the Random Forest (RF) method to screen six variables, including TM457, TM543, B7, NDWI, NDVI, and W7B6VAR. Then, we build the GWR model to estimate the bamboo forest AGB, and the results are compared with those of the cokriging (COK) and orthogonal least squares (OLS) models. The results show the following: (1) The GWR model had high precision and strong prediction ability. The prediction accuracy (R2) of the GWR model was 0.74, 9%, and 16% higher than the COK and OLS models, respectively, while the error (RMSE) was 7% and 12% lower than the errors of the COK and OLS models, respectively. (2) The bamboo forest AGB estimated by the GWR model in Zhejiang Province had a relatively dense spatial distribution in the northwestern, southwestern, and northeastern areas. This is in line with the actual bamboo forest AGB distribution in Zhejiang Province, indicating the potential practical value of our study. (3) The optimal bandwidth of the GWR model was 156 m. By calculating the variable parameters at different positions in the bandwidth, close attention is given to the local variation law in the estimation of the results in order to reduce the model error.

The Influence of Spatial Heterogeneity on the Results of Pumping Tests in an Confined Infinite Aquifer

The Influence of Spatial Heterogeneity on the Results of Pumping Tests in an Confined Infinite Aquifer

Homogenization of a reaction diffusion equation can explain influenza A virus load data

We study the influence of spatial heterogeneity on the antiviral activity of mouse embryonic fibroblasts (MEF) infected with influenza A. MEF of type Ube1L-/- are composed of two distinct sub-populations, the strong type that sustains a strong viral infection and the weak type, sustaining a weak viral load. We present new data on the virus load infection of Ube1L-/-, which have been micro-printed in a checker board pattern of different sizes of the inner squares. Surprisingly, the total viral load at one day after inoculation significantly depends on the sizes of the inner squares. We explain this observation by using a reaction diffusion model and we show that mathematical homogenization can explain the observed inhomogeneities. If the individual patches are large, then the growth rate and the carrying capacity will be the arithmetic means of the patches. For finer and finer patches the average growth rate is still the arithmetic mean, however, the carrying capacity uses the harmonic mean. While fitting the PDE to the experimental data, we also predict that a discrepancy in virus load would be unobservable after only half a day. Furthermore, we predict the viral load in different inner squares that had not been measured in our experiment and the travelling distance the virions can reach after one day.

Spatially heterogeneity response of ecosystem services supply and demand to urbanization in China

A deep understanding of the impact of urbanization on ecosystem service (ES) supply and demand is crucial to regional ecological management and sustainable development. The existing studies mainly focused on the various impacts of urbanization on ES supply, however, there is still a lack of comprehensive research on the spatial heterogeneity response of ES supply, demand and supply-demand ratio (i.e., surplus or deficit) to urbanization, especially at the national scale. To fill this gap, we measured the spatiotemporal changes of ES supply, demand and supply-demand ratios in China utilizing the ecological footprint model at the county scale of resolution. Additionally, the Geographical Weighted Regression (GWR) model was used to explore the spatial heterogeneity response of ES supply, demand and supply-demand ratios to different types of urbanization (i.e., population, economic and land). The results showed that areas with larger total ES demand was mainly concentrated in the Northeast Plain, Liaodong Peninsula, North China Plain, Pearl River Delta, Southeast coastal areas, and Sichuan Basin, while the northwest, northeast and north of China had the greater total ES supply. The counties with strong ES deficit were found in southeastern China, while, most counties in northwestern China were in ES surplus. Additionally, there was significant spatial heterogeneity influence of different types of urbanization on ES in China. Population urbanization in the southeast and south of China had a strong positive impact on the supply-demand ratio in 2000 and 2015, while economic urbanization in the northwest and south regions exerted a more remarkable positive influence. The greater positive impact of land urbanization on the supply-demand ratios was mostly concentrated in northwest China in 2000, and this effect also appeared in southeast China in 2015. Moreover, land urbanization presented a more direct and dominant impact on the ES supply, demand and supply-demand ratios than population and economic urbanization. The findings highlighted that we should focus on the impact of urban expansion on the ecosystem, formulating rational land use planning and optimizing urban spatial configurations, can be used to offset the ES deficit and realize regional sustainable development.

Spatial heterogeneity in resources alters selective dynamics in Drosophila melanogaster.

Environmental features can alter the behaviors and phenotypes of organisms, influencing the dynamics of natural and sexual selection. Experimental environmental manipulation, particularly when conducted in experiments where the dynamics of the purging of deleterious alleles are compared, has demonstrated both direct and indirect effects on the strength and direction of selection. However, many of these studies are conducted with fairly simplistic environments, where it is not always clear how or why particular forms of spatial heterogeneity influence behavior or selection. Using Drosophila melanogaster, we tested three different spatial environments designed to determine if spatial constraint of critical resources influences the efficiency of natural and sexual selection. We conducted two allele purging experiments to (1) assess effects of these spatial treatments on selective dynamics of six recessive mutations, and (2) determine how these dynamics changed when sexual selection was relaxed and spatial area reduced for two of the mutants. Allele purging dynamics depended on spatial environment, however the patterns of purging rates between the environments differed across distinct deleterious mutations. We also tested two of the mutant alleles, and demonstrate sexual selection increased the purging rate.

Simulation of Land-Use Changes Using the Partitioned ANN-CA Model and Considering the Influence of Land-Use Change Frequency

Land-use change is a typical geographic evolutionary process characterized by spatial heterogeneity. As such, the driving factors, conversion rules, and rate of change vary for different regions around the world. However, most cellular automata (CA) models use the same transition rules for all cells in the model space when simulating land-use change. Thus, spatial heterogeneity change is ignored in the model, which means that these models are prone to over- or under simulation, resulting in a large deviation from reality. An effective means of accounting for the influence of spatial heterogeneity on the quality of the CA model is to establish a partitioned model based on cellular space partitioning. This study established a partitioned, dual-constrained CA model using the area-weighted frequency of land-use change (AWFLUC) to capture its spatial heterogeneity. This model was used to simulate the land-use evolution of the Dianchi Lake watershed. First, the CA space was divided into subzones using a dual-constrained spatial clustering method. Second, an artificial neural network (ANN) was used to automatically acquire conversion rules to construct an ANN-CA model of land-use change. Finally, land-use changes were simulated using the ANN-CA model based on data from 2006 to 2016, and model reliability was validated. The experimental results showed that compared with the non-partitioned CA model, the partitioned counterpart was able to improve the accuracy of land-use change simulation significantly. Furthermore, AWFLUC is an important indicator of the spatial heterogeneity of land-use change. The shapes of the division spaces were more similar to reality and the simulation accuracy was higher when AWFLUC was considered as a land-use change characteristic.

Study on the Influence and Correction of Spatial Heterogeneity of Air Temperature in Drought Remote Sensing Monitoring

Temperature Vegetation Dryness Index (TVDI) is one of the commonly used methods in remote sensing drought monitoring. Since this method requires the study area to be large enough to achieve the ideal fitting of the dry and wet edges of the temperature-vegetation feature space, the spatial heterogeneity of meteorological elements has a significant influence on the results. Taking Hebei Plain as an example, the air temperature data at the moment of satellite transit are used to modify the surface temperature retrieved by remote sensing, that is, the surface temperature subtracts the spatial heterogeneity of air temperature, to eliminate the influence of the spatial heterogeneity of air temperature on the TVDI index. TVDI index and surface temperature before and after correction are compared in this paper. The results show that the spatial heterogeneity of air temperature does have a significant effect on TVDI index, and the surface temperature distribution after temperature correction is more reasonable, and will not have great effect on the method of using TVDI to monitor the degree of drought. It can be seen that the correction of surface temperature is more helpful to improve the accuracy of agricultural drought monitoring.

Simulations of spatial patterns and species distributions in sandy land using unmanned aerial vehicle images

The spatial distribution of vegetation in sandy lands is closely related to micro-topography. Point pattern analysis of vegetation distribution from ground surveys and satellite images is a commonly used method but does not capture the influence of spatial heterogeneity at small scales. This study examined long-term ecological observation sites of elm (Ulmus pumila) sparse forest in the Otindag Sandy Land, China. Elevation models from unmanned aerial vehicles (UAVs) and ground survey data on vegetation structure from 3768 elms were used to classify the terrain of sampled sites using a decision tree classification. It combined terrain factors, including slope, aspect, and small-scale altitude differences. Plots were divided into five topographic types: sand flat (53%), sand lowland (17%), sunny slope (13%), shady slope (10%), and sandy ridges (7%). Elm densities varied from 141.7 trees hm−2 on shady slopes to 17.0 trees hm−2 on sand lowland. A 2D Poisson fitting method was applied to the diameter at breast height, crown width, and other vegetation growth characteristics to simulate and verify the distribution of elms in the plots. Multivariate analysis was undertaken to confirm the effect of topographic factors on variation of tree characteristics. The integrated terrain approach could better characterize the spatial distribution of sparse forests. This research demonstrated that UAVs were a useful tool to measure spatial heterogeneity of sand micro-topography. Simulations of the distribution of plant characteristics indicated that the terrain classification matched the spatial pattern analysis of elms in semi-arid regions. Simulation of vegetation distribution is a useful technique for analyzing arid regions. This study will assist with further research on ecological restoration and vegetation protection in semi-arid areas.

A theoretical approach to understanding rumor propagation dynamics in a spatially heterogeneous environment

<p style='text-indent:20px;'>Most of the previous work on rumor propagation either focus on ordinary differential equations with temporal dimension or partial differential equations (PDE) with only consideration of spatially independent parameters. Little attention has been given to rumor propagation models in a spatiotemporally heterogeneous environment. This paper is dedicated to investigating a SCIR reaction-diffusion rumor propagation model with a general nonlinear incidence rate in both heterogeneous and homogeneous environments. In spatially heterogeneous case, the well-posedness of global solutions is established first. The basic reproduction number <inline-formula><tex-math id="M1">\begin{document}$ R_0 $\end{document}</tex-math></inline-formula> is introduced, which can be used to reveal the threshold-type dynamics of rumor propagation: if <inline-formula><tex-math id="M2">\begin{document}$ R_0 &lt; 1 $\end{document}</tex-math></inline-formula>, the rumor-free steady state is globally asymptotically stable, while <inline-formula><tex-math id="M3">\begin{document}$ R_0 &gt; 1 $\end{document}</tex-math></inline-formula>, the rumor is uniformly persistent. In spatially homogeneous case, after introducing the time delay, the stability properties have been extensively studied. Finally, numerical simulations are presented to illustrate the validity of the theoretical analysis and the influence of spatial heterogeneity on rumor propagation is further demonstrated.

Influence of spatial heterogeneity of artificial reefs on food sources and trophic levels of marine animals based on stable isotope ratios

The food sources and trophic levels of four species of fish (Hexagrammos otakii, Pholos fangi, Sebastes schlegelii, Zoarces elongates) and six species of invertebrates (Octopus variabilis, Rapana venosa, Oratosquilla oratoria, Alpheus japonicus, Alpheus distinguendus, Charybdis japonica) prior to and following the formation of spatial heterogeneity of artificial reefs (ARs) were studied using stable isotopes. The trophic levels of fishes were significantly higher than those of invertebrates (P < 0.01). The trend of all 10 species occupies a higher trophic levels in March, and showed lower trophic levels in November. Following the formation of spatial heterogeneity, the trophic levels of fishes decreased significantly and did not increase until next year. One year following the formation, the food sources of Z. elongatus were significantly different from the other three kinds of fish. And its trophic level was significantly higher than the other three kinds of fish and also significantly higher than itself prior to the formation. This means Z. elongatus has already adapted to the ARs, where they can maintain high trophic levels. Within two years following the formation, the trophic levels of 10 species marine animals gradually reached a new steady state. This study revealed the effects of the spatial heterogeneity of artificial reefs on marine animals from the perspective of ecosystem structure.

Determining the Impact of Spatial Heterogeneity on Genomic Prognostic Biomarkers for Localized Prostate Cancer

Localized prostate tumors show remarkably diverse clinical courses, with some being cured by local therapy alone, while others rapidly relapse and have a lethal course despite precision surgery or radiotherapy. Many genomic biomarkers have been developed to predict this clinical behavior, but these are confounded by the extreme spatial heterogeneity of prostate tumors: most are multifocal and harbor multiple subclonal populations. To quantify the influence of spatial heterogeneity on genomic prognostic biomarkers, we developed a case-control high-risk cohort (n = 42) using a prospective registry, risk matched by clinicopathologic prognostic indices. Half of the cohort had early biochemical recurrence (BCR; ie, ≤18 mo), while half remained without evidence of disease for at least 48 mo after radical prostatectomy. We then genomically profiled multiple tumor foci per patient, analyzing 119 total specimens. These data allowed us to validate three published genomic prognostic biomarkers and quantify their sensitivity to tumor spatial heterogeneity. Remarkably, all three biomarkers robustly predicted early BCR, and all three were robust to spatiogenomic variability. These data suggest that DNA-based genomic biomarkers can overcome intratumoral heterogeneity: single biopsies may be sufficient to estimate the risk of early BCR after radical treatment in patients with high-risk disease. Patient summaryWe investigated whether heterogeneity between tumor regions within the prostate affects the accuracy of DNA-based biomarkers predicting early relapse after prostatectomy. We observed persistent accuracy in predicting disease relapse, suggesting that spatial heterogeneity may not hinder biomarker performance.

Self‐Organizing Maps for Evaluation of Biogeochemical Processes and Temporal Variations in Water Quality of Subtropical Reservoirs

AbstractWater quality in reservoirs is often compromised in many regions worldwide by nutrients and trace metals. This demands continuous monitoring; however, analyses of large data sets collected during regular monitoring remain a difficult task. Multivariate techniques offer a fast and robust approach for interpreting complex results. The objective of this study was to check the efficacy of self‐organizing maps (SOMs) as a tool to investigate biogeochemical processes. This tool can also help to illustrate influences of land use patterns on the water quality of reservoirs. Here we use the Itupararanga Reservoir in Brazil as a subtropical example. Vertical profiles were sampled from seven sites in the reservoir in a total of seven campaigns over 24 months. Next to physicochemical parameters in the water column (dissolved oxygen, Eh, pH, and temperature), levels of nutrients (NO3−, NH4+, and PO43−), transition and trace metals (Al, Ba, Ca, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, and Zn), and chlorophyll‐a (Chla) were measured. These variables were correlated with land use using SOM. With this technique samples were classified into 17 distinct groups that showed distinct influences of spatial heterogeneity and seasonality. The analyses helped to reveal a seasonal stratification period, where Fe, Mn, and P were released from sediments. Nutrients and some metal inputs (Al and Fe) were related to agricultural, urban, and grass/pasture areas around the reservoir. Our approach also helped to explain physical and biogeochemical seasonality in the reservoir.

Spatial Heterogeneity of Government Regulation, Spatial Distance and Enterprise Carbon Information Disclosure: An Analysis Based on the Heavy Pollution Industry in China.

Under the background of the construction of ecological civilization in China, since China has a vast amount of territory and large distances between cities, the intensity of environmental supervision in different regions may affect the enthusiasm of enterprises to disclose carbon information. Based on the listed companies of the Chinese heavy pollution industry from 2009 to 2014, using the content analysis method, the paper researches the influence of the spatial heterogeneity regarding government regulation and spatial distance on enterprise carbon information disclosure and puts forward some suggestions. The empirical results show that carbon information disclosure is significantly positively related with the spatial heterogeneity of government regulation. The spatial heterogeneity of government regulation is significantly positively associated to non-financial carbon information disclosure. The relationship between government supervision and financial carbon information disclosure is not significant. Spatial distance and carbon information disclosure are significantly positively related. There is also a significant positive correlation between spatial distance and financial carbon information disclosure. Further study finds that in public enterprises, the spatial heterogeneity of government regulation will promote carbon information disclosure, and the influence of spatial heterogeneity of government regulation on non-financial and financial carbon information disclosure both are significantly positively related. In non-public enterprises, spatial distance and carbon information disclosure are significantly positively related, and spatial distance and non-financial carbon information disclosure are significantly positively related as well.

Evaluating the impact of spatial resolution on tropospheric NO2 column comparisons within urban areas using high-resolution airborne data.

NASA deployed the GeoTASO airborne UV-Visible spectrometer in May-June 2017 to produce high resolution (approximately 250 × 250 m) gapless NO2 datasets over the western shore of Lake Michigan and over the Los Angeles Basin. The results collected show that the airborne tropospheric vertical column retrievals compare well with ground-based Pandora spectrometer column NO2 observations (r2=0.91 and slope of 1.03). Apparent disagreements between the two measurements can be sensitive to the coincidence criteria and are often associated with large local variability, including rapid temporal changes and spatial heterogeneity that may be observed differently by the sunward viewing Pandora observations. The gapless mapping strategy executed during the 2017 GeoTASO flights provides data suitable for averaging to coarser areal resolutions to simulate satellite retrievals. As simulated satellite pixel area increases to values typical of TEMPO, TROPOMI, and OMI, the agreement with Pandora measurements degraded, particularly for the most polluted columns as localized large pollution enhancements observed by Pandora and GeoTASO are spatially averaged with nearby less-polluted locations within the larger area representative of the satellite spatial resolutions (aircraft-to-Pandora slope: TEMPO scale=0.88; TROPOMI scale=0.77; OMI scale=0.57). In these two regions, Pandora and TEMPO or TROPOMI have the potential to compare well at least up to pollution scales of 30×1015 molecules cm-2. Two publicly available OMI tropospheric NO2 retrievals are both found to be biased low with respect to these Pandora observations. However, the agreement improves when higher resolution a priori inputs are used for the tropospheric air mass factor calculation (NASA V3 Standard Product slope = 0.18 and Berkeley High Resolution Product slope=0.30). Overall, this work explores best practices for satellite validation strategies with Pandora direct-sun observations by showing the sensitivity to product spatial resolution and demonstrating how the high spatial resolution NO2 data retrieved from airborne spectrometers, such as GeoTASO, can be used with high temporal resolution ground-based column observations to evaluate the influence of spatial heterogeneity on validation results.

Associations among ecosystem services from local perspectives.

Present works on the relationship between pairwise ecosystem services (ES) in one period are mostly based on global perspective, which ignores the influence of spatial heterogeneity. In view of this problem, this study proposes a static local association model for pairwise ES, and the applicability of this model is then tested in Ningxia Hui Autonomous Region, China. Four ES, namely, grain production, livestock production, soil conservation, and carbon sequestration, are selected. The global and local associations among these services are tested and compared using Spearman's correlation analysis and the proposed local static association model, respectively. Results showed that all six possible ES pairs were significantly correlated in the global scale. However, for strongly correlated pairwise ES (e.g., grain production and livestock production and carbon sequestration), a reverse trend still exists in some local areas. This inference is especially true for ES pairs with relatively low correlation coefficient. We also determine that some of the trade-offs or synergies are formed only by artifact, such as the synergy between grain and livestock production in the midwest, the trade-off between grain production and carbon sequestration concentrating on the southern mountains, and the trade-off between livestock production and soil conservation and carbon sequestration in the central area of the northern Yellow River irrigation district. In summary, local association analysis conveys the abstract overall relationship among specific spatial locations to identify the mechanism of the relationship among ES. This finding will provide new insights for the planning and management of ES, which include that local ES cannot be managed and planned according to the global relationship, local trade-offs or synergies caused by artifact cannot be applied to ES management, the proposed local association analysis helps identify ES problems in parts, and the high-value synergy area is an appropriate reference for ecosystem management.