Spatial Variance Research Articles

Influence of forest canopy structure and wind flow on patterns of sub‐canopy snow accumulation in montane needleleaf forests

AbstractVegetation structure is considered one of the most important factors shaping the spatial variation of snow accumulation under forest canopies. However, fine scale relationships between canopy density, snow interception, wind redistribution and sub‐canopy accumulation are poorly understood and difficult to observe, and their influence governing stand‐scale snow distributions that determine snow covered area depletion during melt is largely unknown. In this study, fine‐scale observations of forest structure and sub‐canopy snow accumulation were analysed over two mid‐winter snowfalls to a sub‐alpine forest in Marmot Creek Research Basin, Canadian Rockies, Alberta, to identify the impact of snow‐canopy interactions on spatial patterns of sub‐canopy snow accumulation. High spatial resolution (5 and 25 cm) snow accumulation estimates and canopy structure metrics were calculated from the combination of repeated UAV‐lidar observations with snow and photographic surveys, utilizing novel resampling methods including voxel ray sampling of lidar (VoxRS) to improve metric robustness and reduce bias. Over 50% of the spatial variance in forest snow accumulation was found at length scales less than 2 m, supporting the role of local scale canopy structure in governing variation in subcanopy snow accumulation. Additionally, subcanopy snow accumulation showed significant angular spread in relationships with overhead canopy structure; the vertical asymmetry coinciding with local windflow directions during snowfall. Detailed angular analysis showed nontrivial snow‐vegetation relationships that likely reflect multiple snowfall‐vegetation processes, including unloading and entrainment of intercepted snowfall during wind gusts and funnelling of entrained particles by downwind vegetation. These fine‐scale findings suggest several emergent processes which may influence snow accumulation at the scale of forest stands, with novel considerations for representing snow water equivalent distributions under dense evergreen canopies under varying environmental and canopy conditions. Similar studies over a broad range of conditions and forests will help refine and generalize the effects observed here for further snow hydrology and forestry applications.

A Lagrangian model for drifting ecosystems reveals heterogeneity-driven enhancement of marine plankton blooms

Marine plankton play a crucial role in carbon storage, global climate, and ecosystem function. Planktonic ecosystems are embedded in patches of water that are continuously moving, stretching, and diluting. These processes drive inhomegeneities on a range of scales, with implications for the integrated ecosystem properties, but are hard to characterize. We present a theoretical framework that accounts for all these aspects; tracking the water patch hosting a drifting ecosystem along with its physical, environmental, and biochemical features. The theory resolves patch dilution and internal physical mixing as a function of oceanic strain and diffusion. Ecological dynamics are parameterized by an idealized nutrient and phytoplankton population and we specifically capture the time evolution of the biochemical spatial variances to represent within-patch heterogeneity. We find that, depending only on the physical processes to which the water patch is subjected, the plankton biomass response to a resource perturbation can vary in size up to six times. This work indicates that we must account for these processes when interpreting and modeling marine ecosystems and provides a framework with which to do so.

Optimal Urban Waste Dumping Site Selection in Kalutara DS Division of Sri Lanka using GIS-based Multi-Criteria Decision Analysis

Improper waste dumping is an emerging issue in Sri Lanka because of the unsystematic urbanisation process. The main problem of the study is determining sites that are ideal for waste dumping. The use of improper waste disposal methods adversely affects the environment and human lifestyle. This study focuses on integrating a structured approach under multi-criteria decision analysis for waste dumping. The present study has been carried out in Kalutara DS Division, integrating Analytical Hierarchy Process (AHP)-based Multi-Criteria Decision Analysis (MCDA) and geospatial techniques to find suitable waste dumping sites. Ten different types of criteria were selected for the study in vector and raster format, namely, land use, population density, schools, religious places, government offices, coastal areas, hospitals, roads, railways, and environmentally sensitive areas. After finalising the criteria, the GIS-based AHP-MCDA methodology was used for weighting, standardisation, and normalisation by obtaining the opinions of experts and conducting literature surveys. The results of the study are sorted into not-suitable, less-suitable, and suitable dumping sites. Classified suitability maps revealed that 36.94% are not suitable, 53.06% are less suitable, and completely 9.9% are suitable for urban waste dumping sites. The spatial variance of suitable and not suitable waste disposal sites was found in the north, north-eastern, and western and central parts of the study area. Hence, based on the results indicated using such methods, it will be an effective manual for engineers, regional planners, and decision-makers to implement sustainable waste management in the study area in the future.

Spatial variance and estimation of nitrogen dioxide levels as a contributing factor to asthma epidemiology in Rawalpindi, Pakistan.

Asthma prevalence and morbidity are increasing rapidly worldwide, especially in developing countries. Previous studies have shown nitrogen dioxide as an important contributor to asthma prevalence along with extreme temperatures, relative humidity, and land use change. The present study aimed to assess the asthma epidemiology and association of nitrogen dioxide, temperature, and land use as a contributing factor for increasing asthma prevalence in Rawalpindi, Pakistan. Secondary data related to the frequency of asthmatics hospital visits were analyzed to figure out the hotspots of asthma by using Getis ord Gi* statistics in ArcGIS 10.2. Moreover, intraurban variation of nitrogen dioxide concentration was analyzed by passive sampling method and its association with the rate of asthmatics hospital visits in Rawalpindi, Pakistan was also researched. Results revealed the random distribution of disease with significant hotspots along with spatial variability of nitrogen dioxide in urban and rural locations. Indoor and outdoor levels of nitrogen dioxide exceed the national and world health organization standards on asthma high risk areas especially in winter season. Congested housing with poor ventilation, unplanned urbanization, cold temperature, and unclean fuel use are revealed as strong determinants of asthma prevalence in Rawalpindi, Pakistan. Extensive monitoring and interventions are needed for the reduction of both indoor and outdoor nitrogen dioxide levels to overcome the increasing rate of asthma prevalence.

Multiscale Asymptotic Analysis Reveals How Cell Growth and Subcellular Compartments Affect Tissue-Scale Hormone Transport

Determining how cell-scale processes lead to tissue-scale patterns is key to understanding how hormones and morphogens are distributed within biological tissues and control developmental processes. In this article, we use multiscale asymptotic analysis to derive a continuum approximation for hormone transport in a long file of cells to determine how subcellular compartments and cell growth and division affect tissue-scale hormone transport. Focusing our study on plant tissues, we begin by presenting a discrete multicellular ODE model tracking the hormone concentration in each cell’s cytoplasm, subcellular vacuole, and surrounding apoplast, represented by separate compartments in the cell-file geometry. We allow the cells to grow at a rate that can depend both on space and time, accounting for both cytoplasmic and vacuolar expansion. Multiscale asymptotic analysis enables us to systematically derive the corresponding continuum model, obtaining an effective reaction–advection–diffusion equation and revealing how the effective diffusivity, effective advective velocity, and the effective sink term depend on the parameters in the cell-scale model. The continuum approximation reveals how subcellular compartments, such as vacuoles, can act as storage vessels, that significantly alter the effective properties of hormone transport, such as the effective diffusivity and the induced effective velocity. Furthermore, we show how cell growth and spatial variance across cell lengths affect the effective diffusivity and the induced effective velocity, and how these affect the tissue-scale hormone distribution. In particular, we find that cell growth naturally induces an effective velocity in the direction of growth, whereas spatial variance across cell lengths induces effective velocity due to the presence of an extra compartment, such as the apoplast and the vacuole, and variations in the relative sizes between the compartments across the file of cells. It is revealed that hormone transport is faster across cells of decreasing lengths than cells with increasing lengths. We also investigate the effect of cell division on transport dynamics, assuming that each cell divides as soon as it doubles in size, and find that increasing the time between successive cell divisions decreases the growth rate, which enhances the effect of cell division in slowing hormone transport. Motivated by recent experimental discoveries, we discuss particular applications for transport of gibberellic acid (GA), an important growth hormone, within the Arabidopsis root. The model reveals precisely how membrane proteins that mediate facilitated GA transport affect the effective tissue-scale transport. However, the results are general enough to be relevant to other plant hormones, or other substances that are transported in a similar way in any type of cells.

Exploring the trend effects of diffuse anthropogenic pollution in a large river passing through a densely populated area

The detection of non-point pollution in large rivers requires high-frequency sampling over a longer period of time, which, however presumably provides data with large spatial and temporal variance. Variability may mean that data sets recorded upstream and downstream from a densely populated area overlap, suggesting at first glance that the urban area did not affect water quality. This study presents a simple way to explore trend-like effects of non-point pollution in the Danube based on data that varied strongly in space and time. For one year, biweekly sampling was carried out upstream and downstream from a large city with negligible emission of untreated wastewater and the surrounding settlements, industrial and agricultural areas.Although most of the values of the 34 examined physicochemical characteristics fell within the range of data previously published for the Danube, and the mean values of all parameters indicated unpolluted surface water, different water quality was revealed upstream and downstream from the metropolitan area at each sampling time. Since the physicochemical characteristics causing the separation also differed from time to time, univariate tests and consensus ordination were used to determine which variables changed similarly during most of the examined period. With this evaluation method, several diffuse pollutants of anthropogenic origin contaminating the Danube in the long term were identified, such as nitrogen, phosphorus, sulphate, chloride, potassium and vanadium. The results demonstrated that trend-like effects of non-point pollution can be detected even in a large river, where physicochemical measurements can vary strongly in space and time.

SPATIO-TEMPORAL ANALYSIS OF TRAFFIC FLOW ON SARGODHA-JHANG ROAD (PUNJAB) PAKISTAN

Traffic flow in Pakistan has become more complex in recent years due to rapid population growth and rapidly generating economic activities. The main objective of this research was to explore the various factors and attributes related to road network configuration contributing to an irregular traffic flow and congestion at second class inter-district national highway section Sargodha-Jhang road, Punjab, Pakistan. It is a national highway road section in central Punjab extending 124 km. Primary data related to traffic flow and volume was collected manually in mode of time and space while secondary data about annual volume of traffic (3 years) gathered from Punjab highway department, southern Zone Lahore. Global positioning system (GPS) is used to mark the different locations of collecting samples with latitude, longitude and elevation. The analysis is performed in MS Excel by applying descriptive statistics and analytical tendencies and results are geographically presented by various graphs and maps prepared by using ArcGIS 10.5 software. Traffic flow was analyzed on hourly, weekly and yearly basis by comparing spatial and temporal average and variance. Results determined the hourly fluctuations as peak from 8:00AM – 9:00AM (1,103 vehicles) and 2:00PM - 3:00PM (1,215 vehicles), medium from 11:00AM – 12:00AM (1,000 vehicles), and low hours from 5:00PM – 6:00PM (800 vehicles). Thursday and Saturday were the busiest days in a weekly flow of the traffic while the Friday was least busy day regarding the weekly flow of traffic. Likewise, the yearly results of traffic flow also shown slight increase in volume of traffic vehicles over the last three years. Lastly few suggestions were proposed to bring the regularity, road safety, less traffic jam and congestion and to avoid the traffic accidents in the study area.

Soil erosion in diverse agroecological regions of India: a comprehensive review of USLE-based modelling.

Soil erosion caused by water refers to the removal of topsoil by rainfall and runoff. Proper selection of an assessment method is crucial for quantifying the spatial variance of soil erosion. The Universal Soil Loss Equation (USLE) and its revised version (RUSLE) are widely used for modelling soil erosion. This study aimed to evaluate the effectiveness of the USLE-based soil erosion modelling in different agroecological regions of India, identify potential issues, and provide suggestions for future applications. The review revealed that little attention has been given to estimate soil erosion in high-priority land degradation regions of India. Additionally, many studies failed to thoroughly verify the authenticity of stated soil loss rates in their research regions either by overestimating or underestimating at least one of the five soil loss parameters. Furthermore, flaws in the application of methods to calculate these parameters leading to erroneous values were identified and suggestions for improvement were made. The USLE-based soil erosion modelling is an effective tool for quantifying soil erosion risk, but researchers should put emphasis on thoroughly verifying the methodologies adopted, unit conversions, and data availability for the estimation of soil loss parameters to improve the accuracy of their final results. This paper provides valuable insights to assist researchers in implementing USLE-based erosion models in diverse agroecological regions in India and elsewhere. However, for effective soil conservation and sustainable agriculture, further research is necessary to develop efficient techniques for using USLE-based soil erosion modelling to achieve a comprehensive understanding of erosion risk across different agroecological regions.

Nitrogen-Fixing Plants Enhance and Stabilize Rhizospheric Soil Organic Carbon in Tropical Rainforests, Hainan Island, China

Abstract Nitrogen (N)-fixing plants play an important role in stimulating soil nitrogen supply, but the effect of N-fixing plants on soil organic carbon (SOC) sequestration has not been well documented. In this study, we measured rhizospheric carbon components and N content of first to second (1–2)-order and third to fifth (3–5)-order roots of N-fixing and non-N-fixing plants in a montane tropical rainforest in Hainan Island, China, to examine the effects of N-fixing plants on SOC and soil heavy fraction carbon (HFC), and to ascertain the main regulating factors. The results showed that rhizospheric SOC between (1–2) and (3–5)-order roots was 14.2% and 10.5% greater, respectively, for N-fixing plants compared with non-N-fixing plants. Similarly, the HFC was 0.9% (1–2) and 3.6% (3–5) greater for N-fixing plants than non-N-fixing plants. Redundancy analysis showed that spatial variance in rhizospheric SOC and HFC associated with 1–2-order roots was explained by specific root length (SRL) of second-order roots (55.8%) and specific leaf area (SLA) (14.6%), whereas spatial variance was better explained for 3–5 roots by total soil N (44.6%), diameter of fifth-order roots (16.4%), and leaf C (17.9%), respectively. Within the rhizospheric soil of 1–2 roots, N-fixing plants with smaller SRL of second-order roots presented greater SOC and HFC, whereas smaller SLA was associated with greater HFC. By contrast, rhizospheric soil of 3–5 roots under N-fixing plants had greater SOC and HFC levels in environments with higher total soil N and smaller diameters of fifth-order roots. Our study revealed that N-fixing plants enhanced rhizospheric SOC and HFC compared with non-N-fixing plants. The rhizospheric SOC and HFC were correlated strongly by root morphology traits, leaf morphology, and soil properties. Study Implications: In this article, we compared rhizospheric soil of N-fixing and non-N-fixing species in a montane tropical rainforest in Hainan Island, China, to examine the effects of N-fixing plants on SOC and HFC. Our results suggest the N-fixing plants enhance and stabilize rhizospheric SOC and HFC; rhizospheric SOC and HFC for first- to second-order roots of N-fixing plants were mostly negatively regulated by SRL of second-order roots; and N-fixing plants had positive effects on rhizospheric SOC and HFC for third- to fifth-order roots through increased soil N. Understanding these mechanisms could improve and accumulation of soil C sink in tropical areas.

Benefits of individual pitch control on offshore wind turbine submerged in upstream wake

The present study investigates the benefits of an individual pitch control (IPC) strategy on a DTU 10MW with a floating platform subjected to an upstream wind turbine wake. The wind turbine wakes are generated via large eddy simulation, with which the dynamic response of the downstream wind turbines are calculated using OpenFAST with IPC engaged. Three wind speeds and six arrangements with varying longitudinal and lateral offsets of wind turbine pairs are investigated. Findings show that the large spatial variance of the wind velocity in the rotor-swept area leads to a higher fatigue load of out-of-plane blade-root moment (MOoP). The IPC is effective in suppressing the 1P peak of the power spectral density of MOoP, as well as the yaw fluctuation of the platform motion, which saw a maximum 20% reduction in the damage equivalent load of MOoP and up to 74% reduction in the platform yaw fluctuation in the staggered wind turbine layout.

Relationship Between the Concentrations of Black Carbon and some Meteorological Parameters Over Iraq using GIS Techniques

Abstract Black carbon is a significant cause of global warming, and anthropogenic activity along with weather conditions affects its long-term variability. Numerous regional and socioeconomic factors have influenced the distribution of Black Carbon (BC) concentration. In this work, monthly black carbon remote sensing data were used to interpolate the yearly regional distributions of black carbon concentrations in order to investigate the link between black carbon concentrations and climatic factors and their spatial variance in Iraq during the period (1980 to 2022). The correlation coefficient between black carbon and several climatic variables, such as precipitation, temperature, wind speed, and pressure, has been calculated using the Pearson correlation coefficient model. The findings also revealed that pressure has the strongest relationship with BC, with substantial positive correlations of more than 0.7 extending from the northeast through the centre to the extreme southeast. It becomes evident to us that the relationships between small particles and meteorological variables vary depending on the areas, the topography, the local climate, human and natural variances, and weather variations.

Modeling spatial dependencies of natural hazards in coastal regions: a nonstationary approach with barriers

Natural hazards like floods, cyclones, earthquakes, or, tsunamis have deep impacts on the environment and society causing damage to both life and property. These events can cause widespread destruction and can lead to long-term socio-economic disruption often affecting the most vulnerable populations in society. Computational modeling provides an essential tool to estimate the damage by incorporating spatial uncertainties and examining global risk assessments. Classical stationary models in spatial statistics often assume isotropy and stationarity. It causes inappropriate smoothing over features having boundaries, holes, or physical barriers. Despite this, nonstationary models like barrier model have been little explored in the context of natural disasters in complex land structures. The principal objective of the current study is to evaluate the influence of barrier models compared to classical stationary models by analysing the incidence of natural disasters in complex spatial regions like islands and coastal areas. In the current study, we have used tsunami records from the island nation of Maldives. For seven atoll groups considered in our study, we have implemented three distinct categories of stochastic partial differential equation meshes, two for stationary models and one that corresponds to the barrier model concept. The results show that when assessing the spatial variance of tsunami incidence at the atoll scale, the barrier model outperforms the other two models while maintaining the same computational cost as the stationary models. In the broader picture, this research work contributes to the relatively new field of nonstationary barrier models and intends to establish a robust modeling framework to explore spatial phenomena, particularly natural hazards, in complex spatial regions having physical barriers.

An asymmetric relationship between Tibetan Plateau surface temperature regimes and oceanic–atmospheric circulations

AbstractAs a high‐altitude region, the Tibetan Plateau (TP) is sensitive to climate change. Since the 1960s, the TP has experienced significant spatial‐variant warming, vital to changing the region's terrestrial ecosystem. To understand surface air temperature (SAT) regimes over the TP, we investigated their link with teleconnections using observational and reanalysis datasets from 1982 to 2019. We employed wavelet analysis, empirical orthogonal teleconnection (EOT), partial regression, canonical correlation analysis and t test. Our results indicate an asymmetrical connection between teleconnections and SAT, with Atlantic Multidecadal Oscillation (AMO) predominant over the northern TP. In contrast, the El Niño–Southern Oscillation (ENSO) is more prevalent in the southern part. The spatial variance was potentially related to windspeed, total cloud cover (TCC), net longwave radiation (NLWR), net shortwave radiation (NSWR), cloud‐forcing net longwave flux (CF‐NLWF) and cloud‐forcing net shortwave flux (CF‐NLWF) anomalies. ENSO and Pacific Decadal Oscillation (PDO) modulate cloud cover variation in the southern TP, mainly the southwest, via the monsoon and subtropical westerlies. In the north, cloud cover variation is related to the moisture transported by the AMO‐induced prevailing westerlies. The study examined the complex interplay between teleconnections, SAT and cloud radiative forcing by examining the cloud cover and radiation balance relationship. These findings contribute to understanding climate change impacts on the TP, informing climate projections and guiding adaptation strategies by elucidating the relationship between TP temperature variation and oceanic–atmospheric oscillation patterns.

An Improved UAV Bi-SAR Imaging Algorithm with Two-Dimensional Spatial Variant Range Cell Migration Correction and Azimuth Non-Linear Phase Equalization

The transmitter and receiver of unmanned aerial vehicle (UAV) bistatic synthetic aperture radar (Bi-SAR) are respectively carried on different UAV platforms, which has the advantages of flexible movement and strong concealment, and has broad application prospects in remote sensing fields. However, the range cell migration (RCM) and azimuth non-linear phase (ANP) of UAV Bi-SAR are seriously spatially variant along the range and azimuth directions, while the UAV Bi-SAR has a short operating range, complex trajectory and wide azimuth beam. Aiming at the problem that the RCM and ANP of UAV Bi-SAR in spotlight mode are difficult to correct and equalize due to the severe two-dimensional (2D) spatial variation, an RCM correction (RCMC) and ANP equalization (ANPE) method based on Doppler domain blocking is proposed. First, the azimuth spatial variance of RCM is eliminated by Doppler blocking, and the range spatial variant RCMC is realized by RNCS. Second, by combining Doppler blocking with azimuth nonlinear chirp scaling (ANCS), this method can adapt to ANPE with larger width and more severe spatial variation. At last, the criteria of Doppler blocking are given in detail, and the effectiveness of the proposed method is verified by UAV Bi-SAR real data and computer simulation.

Vulnerability to urban flooding assessed based on spatial demographic, socio-economic and infrastructure inequalities

Urban flooding is a priority in natural risk management and mitigation because it is the most frequent natural disaster in densely urbanised environments. This research explores flood vulnerability in cities by developing an index that can be easily implemented across the world. Our methodology is based on the arrangement of a series variables into three different classes (demography, socioeconomics and infrastructure) and the determination of their spatial variability through a Principal Component Analysis (PCA). We tested the proposed approach in the city of Santander (Spain) where a vulnerability index map was generated based on the combination of the proposed classes. The analysis show that we can reduce complexity from an initially identified 159 relevant variables to 16 representative and impactful variables in terms of spatial variance. Classification of the variables into three different classes made it possible to quantify the main causes of vulnerability to flooding across space. We produce a flood risk map by integrating our findings with a flood hazard map for the same area. This flood risk map gives urban planners detailed information about the most affected areas and allows them to design measures that mitigate the severity and effects of floods optimising available resources.

Thermal environment of rural residential buildings in Hengyang, China: A two-year on-site study

Epidemiological evidence suggests that outdoor climate strongly influences human health. However, since people spend 90% of their time indoors, the indoor environment may be a better indicator of epidemiological diseases and infections. Despite previous studies exploring the relationship between indoor and outdoor thermal environments, little is known about this relationship in China's subtropical countryside. This study aims to investigate the indoor-outdoor relationship of air temperature and relative humidity, examine the spatial and temporal variation characteristics of the indoor thermal environment, and estimate indoor thermal comfort. We monitored outdoor and indoor air temperature and relative humidity for two years using wireless IoT temperature/humidity button sensors in fourteen residential buildings. Our results show that the average vertical spatial variance range of air temperature was largest in summer (2.11 °C) and smallest in fall (1.23 °C), while horizontal spatial variation was negligible. The average daily temporal variation range was largest on the third floor (2.3 °C) and smallest on the first floor (1.5 °C). Indoor air temperatures were highly correlated with outdoor air temperatures, with a linear correlation coefficient greater than 0.9. The relationship between indoor and outdoor relative humidity was moderate, with an average correlation coefficient of 0.54. Furthermore, we found that almost all uncomfortable indoor air temperature points were lower than the lower temperature limit of the adaptive comfort band in all seasons, and almost no indoor temperatures in summer and winter were within the PMV comfort band.

A novel adaptation of spatial interpolation methods to map health attitudes related to COVID-19

BackgroundThe COVID-19 pandemic presented substantial challenges to public health stakeholders working to vaccinate populations against the disease, particularly among vaccine hesitant individuals in low- and middle-income countries. Data on the determinants of vaccine hesitancy are scarce, and often available only at the national level. In this paper, our goal is to inform programmatic decision making in support of local vaccine uptake. Our analytical objectives to support this goal are to (1) reliably estimate attitudinal data at the hyperlocal level, and (2) estimate the loss of data heterogeneity among these attitudinal indicators at higher levels of aggregation. With hyperlocal attitudinal data on the determinants of vaccine hesitancy, public health stakeholders can better tailor interventions aimed at increasing uptake sub-nationally, and even down to the individual vaccination site or neighborhood.MethodsWe estimated attitudinal data on the determinants of vaccine hesitancy as framed by the WHO’s Confidence, Complacency, and Convenience (“3Cs”) Model of Vaccine Hesitancy using a nationally and regionally representative household survey of 4,922 adults aged 18 and above, collected in February 2022. This custom survey was designed to collect information on attitudes towards COVID-19 and concerns about the COVID-19 vaccine. A machine learning (ML) framework was used to spatially interpolate metrics representative of the 3Cs at a one square kilometer (1km2) resolution using approximately 130 spatial covariates from high-resolution satellite imagery, and 24 covariates from the 2018 Nigeria Demographic and Health Survey (DHS).ResultsSpatial interpolated hyperlocal estimates of the 3Cs captured significant information on attitudes towards COVID-19 and COVID-19 vaccines. The interpolated estimates held increased heterogeneity within each subsequent level of disaggregation, with most variation at the 1km2 level.ConclusionsOur findings demonstrate that a) attitudinal data can be successfully estimated at the hyperlocal level, and b) the determinants of COVID-19 vaccine hesitancy have large spatial variance that cannot be captured through national surveys alone. Access to community level attitudes toward vaccine safety and efficacy; vaccination access, time, and financial burden; and COVID-19 beliefs and infection concerns presents novel implications for public health practitioners and policymakers seeking to increase COVID-19 vaccine uptake through more customized community-level interventions.

Analyzing community wastewater in sub-sewersheds for the small-scale detection of SARS-CoV-2 variants in a German metropolitan area

Wastewater surveillance of SARS-CoV-2 proved useful, including for identifying the local appearance of newly identified virus variants. Previous studies focused on wastewater treatment plants (WWTP) with sewersheds of several hundred thousand people or at single building level, representing only a small number of people. Both approaches may prove inadequate for small-scale intra-urban inferences for early detection of emerging or novel virus variants. Our study aims (i) to analyze SARS-CoV-2 single nucleotide variants (SNVs) in wastewater of sub-sewersheds and WWTP using whole genome sequencing in order to (ii) investigate the potential of small-scale detection of novel known SARS-CoV-2 variants of concern (VOC) within a metropolitan wastewater system. We selected three sub-sewershed sampling sites, based on estimated population- and built environment-related indicators, and the inlet of the receiving WWTP in the Ruhr region, Germany. Untreated wastewater was sampled weekly between October and December 2021, with a total of 22 samples collected. SARS-CoV-2 RNA was analyzed by RT-qPCR and whole genome sequencing. For all samples, genome sequences were obtained, while only 13 samples were positive for RT-qPCR. We identified multiple specific SARS-CoV-2 SNVs in the wastewater samples of the sub-sewersheds and the WWTP. Identified SNVs reflected the dominance of VOC Delta at the time of sampling. Interestingly, we could identify an Omicron-specific SNV in one sub-sewershed. A concurrent wastewater study sampling the same WWTP detected the VOC Omicron one week later. Our observations suggest that the small-scale approach may prove particularly useful for the detection and description of spatially confined emerging or existing virus variants circulating in populations. Future studies applying small-scale sampling strategies taking into account the specific features of the wastewater system will be useful to analyze temporal and spatial variance in more detail.

Spatially resolved generation profiles for building, land and water-bound PV: a case study of four Dutch energy transition scenarios

Abstract. Alongside a transition from steerable and centralized traditional electricity generation to intermittent and more decentralized renewable electricity generation from solar panels and wind turbines, Dutch energy transition scenarios project a widespread deployment of heat pumps and electric vehicles towards 2050. While clearly contributing to the decarbonization of the Dutch energy system, these developments impose challenges regarding electricity supply-demand mismatch and grid congestion. Spatially resolved electricity demand and supply profiles are required to gain a better insight into where and when such problems are likely to occur within the different scenarios. The present paper focuses on Dutch solar energy supply and features the construction of geodatabases of scenario-specific, spatially resolved electricity generation profiles for building, land and water-bound PV. Country-level PV capacities are geographically distributed based on spatial variance in roof PV potential and availability of suitable land and water use areas. Corresponding electricity generation profiles are constructed using historical meteorological measurements, a diffuse fraction model and a anisotropic transposition model. Empirically found performance ratio profiles are applied to account for a multitude of performance loss factors, including shading, dust and inverter efficiency. In 2050, building-bound capacity is projected to show only limited overlap with both land-bound and water-bound PV capacity. On the other hand, regions with considerable water-bound PV capacity also tend to show considerable land-bound PV capacity. Compared to the present-day situation, yearly country-level PV electricity generation is projected to be a factor 18.5, 15.7, or 7.7 higher in 2050 when respectively following the Regional, National or International Steering scenarios.

An occupant-centric approach for spatio-temporal visual comfort assessment and optimization in daylit sports spaces

Most visual comfort analysis is based on glare detections in limited fixed view scenes, but sports spaces present challenges given occupants’ spatial and view directional variations in sports-related visual tasks. This paper proposes an occupant-centric approach for spatio-temporal visual comfort assessment and optimization (ST-VCAO) in daylit sports spaces by better considering the occupants’ movement behaviour. It introduces the movable view scenes (MVS) in glare simulations and further accelerates the ST-VCAO process using the GPU-based parallel simulation combining response surface methodology. The proposed approach was applied to a real case building in Harbin, China. Annual daylight and glare simulations in two tennis courts with a total of 500 MVS were performed using Perez all-weather sky model with Chinese standard weather data. Results indicate that the proposed approach could sufficiently clarify the spatial variance and temporal variation of visual comfort across all MVS, which is appropriate for sports spaces. Meanwhile, using the developed workflow, the total computational time could be saved by 27.08% (Case 1) or 71.25% (Case 2). Amongst all Pareto solutions, the optimum alternative upon the worst one observed 200% (Case 1) or 50.7% (Case 2) higher visual comfort.