Spatial-temporal Patterns Research Articles

A Machine Learning Approach for Modeling the Spatial-temporal Propagation Pattern of COVID-19

Abstract. Predicting infections resulting from epidemiological contagions involves a complex therapeutic endeavor that necessitates the consideration of various factors within the available data. COVID-19, a globally recognized highly dangerous disease, presents an opportunity to effectively understand its patterns and spatial determinants to combat similar ailments. This study seeks to employ a deep neural network and several machine learning techniques to identify the crucial factors influencing COVID-19 infections and to forecast the spatial-temporal spread of the disease. Previous research has focused on predicting outcomes using a limited set of 8 patient variables. To contribute to this field, the current investigation analyzes a dataset comprising 47,029 records of infected individuals who sought medical attention, with 18,433 of them testing positive for the coronavirus and requiring hospitalization. The study explores the prediction of illness and hospitalization needs based on geographical locations by utilizing machine learning algorithms such as Random Forest, Naive Bayes, Decision Tree, and K-Nearest Neighbor. The machine learning process commences with input data (comprising the 47,029 records), enabling the machine to identify patterns within the dataset and subsequently make informed decisions based on these patterns and insights gained. The primary objective is to allow the machine to autonomously learn and refine its predictions without human intervention. Applying these algorithms to COVID-19 data for spatial-temporal prediction revealed that the Random Forest algorithm achieved the highest accuracy of 0.84, while the Simple Bayes algorithm exhibited the lowest accuracy at 0.50. Additionally, the study compared random forest, decision tree, simple bayes, and K-nearest neighbor algorithms to predict the severity of road accidents.

Analysis of Behaviour Pattern of Bicycle-sharing Users Based on Complex Network: a Case Study of Beijing Downtown Area

Abstract. The introduction of the Bicycle-sharing System (BSS) has provided great convenience for residents for short travel in city. However, the parking lot planning and bicycle logistics distribution remain suboptimal, adversely affecting traffic order. Studying the spatial and temporal usage patterns can be helpful for better management of BSS. In this paper, we propose an analytical framework for detecting the bicycle-sharing pattern based on the community detection algorithms from complex networks to study the spatial-temporal patterns of usage.Firstly we identify the potential demand for bicycle among urban residents with a dataset from the BSS in Beijing downtown area. Then we construct a linkage network for modelling the BSS by applying community detection algorithms to identify regions of both high and low connectivity. Following this, based on the division of sub-areas, three indicators (graph density, cross-area travel demand, and confidence ellipses) are established for characterizing these sub-areas. Our findings include: (1) The usage pattern of the BSS within the downtown area consistently exhibits relatively stable clustering phenomena over time. (2) The usage pattern is related to the urban spatial structure, with significant differences between weekdays and weekends. (3) Residents tend to complete their cycling within the current sub-areas. (4) Generally, smaller sub-areas tend to have denser bicycle travel behaviour. These insights are vital for improving BSS parking lot planning and logistics distribution.

Main spatial-temporal regularities of the state of the chemical composition of groundwater of the oligocene aquifer in the Shaim oil and gas bearing area

The constantly growing need for fresh water in the Shaim oil and gas region necessitates the exploration of new areas and the development of measures to improve the operation of water intakes. To predict the quality of groundwater, which is an operational parameter of the aquifer aquifer, it is needed up-to-date information on the chemical composition of ground-water. The aim of this study to identify main spatial-temporal regularities of the state of the chemical composition of groundwater of the oligocene aquifer in the Shaim oil and gas bearing area, compared to average values from adjacent areas. Research methods include the systematization of laboratory data on water samples, analysis of groundwater chemistry, and mapping of the main spatial-temporal patterns in component concentration changes. The average values of chemical composition indicators in the studied area are similar to those of neighbouring areas, with some indicators exceeding drinking water standards. Similarities are observed in the variability of the chemical composition, spatial patterns of changes in most chemical indicators, and the presence of areas with extreme values. Current data on the chemical composition of the oligocene aquifer ground-water, including key characteristics and spatial-temporal trends presented in distribution maps, can be used in designing groundwater intakes and forecasting long-term groundwater quality.

Unexplained high and persistent methyl bromide emissions in China

Methyl bromide (CH3Br) is an important ozone-depleting substance whose use is regulated under the Montreal Protocol. Quantifying emissions on the national scale is required to assess compliance with the Montreal Protocol and thereby ensure the timely recovery of the ozone layer. However, the spatial-temporal patterns of China’s national CH3Br emissions remain unclear. Here we estimate the national emissions of CH3Br in China during 2011−2020 using atmospheric observations at 10 sites across China combined with an inversion technique (top-down) and compare those with an updated inventory of identified emission sources (bottom-up). Measured CH3Br mole fractions are enhanced well above the background mole fractions, especially at sites in eastern China. Top-down emission estimates exceed bottom-up estimates by 5.5 ± 1.4 gigagrams per year, with the largest fraction (60%) of observationally derived CH3Br emissions arising from underestimated or unidentified emissions sources. This study shows the potential impacts of the unaccounted emissions on stratospheric ozone depletion, with implications for the Montreal Protocol.

Spatial–Temporal Patterns in the Enteric Pathogen Contamination of Soil in the Public Environments of Low- and Middle-Income Neighborhoods in Nairobi, Kenya

Public spaces in countries with limited societal development can be contaminated with feces containing pathogenic microbes from animals and people. Data on contamination levels, spatial distribution, and the diversity of enteric pathogens in the public settings of low- and middle-income neighborhoods are crucial for devising strategies that minimize the enteric infection burden. The objective of this study was to compare spatial–temporal differences in the detection rate and diversity of enteric pathogens in the public spaces of low- and middle-income neighborhoods of Nairobi, Kenya. TaqMan array card (TAC) molecular assays were employed to analyze soil samples for 19 enteropathogens, along with a selective bacterial culture for pathogenic Enterobacteriaceae. An observational assessment was conducted during every site visit to document the hygienic infrastructure and sanitation conditions at the sites. We detected at least one pathogen in 79% (127/160) and ≥2 pathogens in 67.5% (108/160) of the soil samples tested. The four most frequently detected pathogens were EAEC (67.5%), ETEC (59%), EPEC (57.5%), and STEC (31%). The detection rate (91% vs. 66%) and mean number of enteric pathogens (5 vs. 4.7) were higher in low-income Kibera than in middle-income Jericho. The more extensive spatial distribution of pathogens in Kibera resulted in increases in the detection of different enteric pathogens from within-site (area < 50 m2) and across-site (across-neighborhood) movements compared to Jericho. The pathogen detection rates fluctuated seasonally in Jericho but remained at sustained high levels in Kibera. While better neighborhood conditions were linked with lower pathogen detection rates, pathogenic E. coli remained prevalent in the public environment across both neighborhoods. Future studies should focus on identifying how the sources of pathogen contamination are modified by improved environmental sanitation and hygiene and the role of these contaminated public environments in enteric infections in children.

Long term 5G base station traffic prediction method based on spatial-temporal correlations

In the domain of 5G network management, accurately predicting traffic volumes at base stations remains a critical yet challenging endeavor, primarily due to the complexities inherent in the spatial and temporal data interactions. Current methods often fall short in effectively harnessing long-term trends and spatial interconnections among base stations. To bridge these gaps, this paper introduces the GCformer model, a novel approach that capitalizes on both spatial relationships and temporal patterns for multi-base station traffic prediction. Spatially, the proposed model employs graph convolutional networks to integrate diverse spatial information and construct insightful adjacency matrices that includes Euclidean distances and non-Euclidean distances (area types of base station locations and similarities in traffic flow among various stations), thereby enhancing the predictability of traffic dynamics. Temporally, the application of the Transformer's attention mechanism enables better capture of long-term relational dependencies in the temporal domain of 5 G base station traffic data. Additionally, a time-variant optimization module is designed to establish diurnal cycle data for each base station's traffic, replacing the traditional positional encoding with a more nuanced model that improves the learning of historical data correlations. Empirical results from exhaustive case studies confirm the superiority of the GCformer model in forecasting traffic volumes. The GCformer exhibits a 4.01 % improvement in mean squared error and a 3.37 % enhancement in mean absolute error compared to the best-performing baseline model, showcasing its potential to significantly improve operational strategies in 5G networks.

Driving Effects and Spatial Spillover of Technological Innovation on the Carbon Balance of Agriculture

<p style="text-align:justify"><span style="font-size:10.5pt"><span style="line-height:150%"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif">There is significant controversy over the overall effects of technological innovation on carbon balance. Based on panel data of agriculture from 28 provinces in China from 2009 to 2021, utilizing ArcGIS and spatial Durbin model, the spatial-temporal patterns of agricultural technological innovation and carbon balance are analyzed, and furtherly the spatial effects of technological innovation on carbon balance are examined. The results indicate: (1) the overall agricultural carbon surplus in Chinese provinces has been continuously increasing from 2009 to 2021; (2) the direct effect of technological innovation on the carbon surplus in agriculture is significantly positive, while the spatial spillover effect is significantly negative; (3) the mediating effect results show that technological innovation affects the output of the agricultural sector and thereby influences agricultural carbon balance; (4) the impact of technological innovation on agricultural carbon balance exhibits an asymmetric spatial spillover effect. Advantageous regions suffer from the positive effects of spatial spillover, while non-advantageous regions benefit from the negative impacts of spillover, resulting in an overall negative effect. This study's findings play an important role in addressing the existing controversy over the overall effects of technological innovation on carbon balance and provide important insights for formulating policies on low-carbon technological innovation in agriculture.</span></span></span></span></span></span></p>

Capturing Urban Heterogeneity Enhances Tropical Cyclones Simulation in Houston

AbstractUrbanization in the coastal region has increased socioeconomic losses from landfalling tropical cyclones (TCs). Although previous studies have explored the broad impacts of urbanization on TCs, the effect of heterogeneity caused by local intra‐urban variability has not been examined. To address this gap, this study utilized the urban Local Climate Zone (LCZ) to capture the urban landscape heterogeneity and the impacts of this representation on the simulation of the post‐landfall TCs characteristics in Houston. Taking the case of two recent TCs: Hurricane Harvey (occurred in 2017) and Tropical Storm Imelda (occurred in 2019), the study evaluated the impact of urban heterogeneity on 10‐m winds, 2‐m temperature, land surface temperature, and precipitation across Houston. The consideration of intra‐urban heterogeneity using LCZ can improve the 10‐m winds, 2‐m temperature, and spatial pattern of land surface temperature. Although the cumulative rainfall remained largely similar within the experiments (with and without LCZ), incorporating intra‐urban heterogeneity notably modified the spatial structure of urban rainfall, particularly in simulating heavy rainfall hot spots. These findings are consistent for both TCs and demonstrate the positive impact of incorporating intra‐urban heterogeneity on the landfalling TC simulations over the Houston area.

Spatial-temporal patterns of foliar and bulk soil 15N isotopic signatures across a heterogeneous landscape: Linkages to soil N status, nitrate leaching, and N2O fluxes

The natural abundance of plant and bulk soil 15N isotopic signatures provides valuable insights into the magnitude of nitrogen cycling and loss processes within terrestrial ecosystems. However, 15N isotopic signatures are highly variable in space due to natural and anthropogenic factors affecting N cycling processes and losses. To date, most studies on foliar and bulk soil 15N isotopic signatures have focused on N-limited forest ecosystems at relatively large spatial scales, while similar studies in N-enriched ecosystems at finer spatial scales are lacking. To address this gap and evaluate links between soil 15N isotopic signatures and ecosystem N cycling and loss processes (plant N uptake, N leaching, and gaseous loss), this study quantified foliar and bulk soil 15N isotopic signatures, soil physicochemical parameters, gaseous (N2O), and hydrological (NO3) N losses at 80 sites distributed across a heterogeneous landscape (∼5.8 km2). To account for the spatial-temporal heterogeneity, the measurements were performed in four campaigns (March, June, September 2022, and March 2023) at sites that considered different land uses, soil types, and topography. Results indicated that foliar and bulk soil 15N isotopic signatures were significantly (P < 0.05) more enriched in arable and grassland ecosystems than forests, suggesting a more open N cycle with significant N cycling and losses due to higher N inputs from fertilizers. Similar to soil inorganic N, N2O fluxes, and NO3 leaching rates, landscape-scale foliar and soil 15N isotopic signatures varied widely spatially, particularly at grassland and arable land (−3 to 9.0‰), with bivariate and multivariate analyses also showing significant relationships between landscape-scale soil 15N isotopic signatures and the aforementioned parameters (r2: 0.29 to 0.82). Based on these relationships, our findings suggested that foliar and bulk 15N isotopic signatures may capture fine-scale areas with persistently high and low environmental N losses (N2O fluxes and NO3 leaching) within a heterogeneous landscape.

Identification of Spatial Distribution of Afforestation, Reforestation, and Deforestation and Their Impacts on Local Land Surface Temperature in Yangtze River Delta and Pearl River Delta Urban Agglomerations of China

Forest change affects local and global climate by altering the physical properties of the land surface. Accurately assessing urban forest changes in local land surface temperature (LST) is a scientific and crucial strategy for mitigating regional climate change. Despite this, few studies have attempted to accurately characterize the spatial and temporal pattern of afforestation, reforestation, and deforestation to optimize their effects on surface temperature. We used the China Land Cover Dataset and knowledge criterion-based spatial analysis model to map urban forestation (e.g., afforestation and reforestation) and deforestation. We then analyzed the impacts of these activities on LST from 2010 to 2020 based on the moving window strategy and the spatial–temporal pattern change analysis method in the urban agglomerations of the Yangtze River Delta (YRD) and Pearl River Delta (PRD), China. The results showed that forest areas declined in both regions. Most years, the annual deforestation area is greater than the yearly afforestation areas. Afforestation and reforestation had cooling effects of −0.24 ± 0.19 °C and −0.47 ± 0.15 °C in YRD and −0.46 ± 0.10 °C and −0.86 ± 0.11 °C in PRD. Deforestation and conversion of afforestation to non-forests led to cooling effects in YRD and warming effects of 1.08 ± 0.08 °C and 0.43 ± 0.19 °C in PRD. The cooling effect of forests is more evident in PRD than in YRD, and it is predominantly caused by reforestation. Moreover, forests demonstrated a significant seasonal cooling effect, except for December in YRD. Two deforestation activities exhibited seasonal warming impacts in PRD, mainly induced by deforestation, while there were inconsistent effects in YRD. Overall, this study provides practical data and decision-making support for rational urban forest management and climate benefit maximization, empowering policymakers and urban planners to make informed decisions for the benefit of their communities.

How to build vibrant communities by utilizing functional zones? A community-detection-based approach for revealing the association between land use and community vibrancy

Investigating the association between land use and urban vibrancy is crucial for sustainable urban development. To that end, according to the four conditions proposed by Jacobs (1962), suitable units should be identified before investigating this association. In this study, we consider spatial-temporal human activity patterns and propose a novel community-detection approach integrating graph neural network (GNN) and gated recurrent unit (GRU) to identify suitable geographic units. Then, the impact of land-use-relevance variables on community vibrancy is measured by the geographically and temporally weighted regression (GTWR) model. Using the cell phone signaling data from Beijing, we initially identify 66 communities, primarily comprising intra-travel (the origin and destination are in the same community) and a small proportion of inter-community travel, showing distinct vibrancy patterns and different land use characteristics. Then, we develop a GTWR model to reveal the associations between land use and community vibrancy, revealing that land-use-based variables, such as mixed land use and densities of points of interest (POIs), have spatial-temporal-heterogeneous impacts on community vibrancy, implying the heterogeneity among the development focus of communities, such as population density and functional zone structure. Consequently, we propose heterogeneous development strategies for communities, which could offer valuable suggestions for enhancing urban vibrancy.

Current status and research prospects of terrestrial ecosystem carbon sink in Northeast China.

Increasing the carbon sink capacity of terrestrial ecosystems is a primary strategy to mitigate climate change and achieve the "carbon neutrality" goal. Clarifying the status and future dynamics of carbon sink of terrestrial ecosystems in Northeast China is crucial for achieving "carbon neutrality" as this region is a core contributor to carbon sink in China's terrestrial ecosystems. Here, we systematically summarized current research on carbon sink of terrestrial ecosystems across Northeast China, including the measurements and spatial-temporal patterns of carbon sinks, driving mechanisms of carbon sinks, the assessments of carbon sink potential, and technologies for increasing carbon sequestration. There are substantial uncertainties in quantifying terrestrial ecosystem carbon sink in Northeast China due to differences in data sources and methods, especially for forest carbon sink measurements, ranging from 0.020 to 0.157 Pg C·a-1. Carbon sink function depends on carbon exchange processes across plant-soil-atmosphere interfaces. The key pathways to enhance carbon sequestration in Northeast China under different temporal and spatial scales remains unclear. Improving terrestrial ecosystem quality is the key and core of carbon sequestration and sink enhancement. However, there is an urgent need to develop a multi-ecosystem collaborative carbon sequestration and sink enhancement technology system for the "dual carbon" goal. Future research needs to develop an accurate carbon sink measurement system that integrates multi-source data and multi-scale technologies to accurately assess the function and potential of carbon sink in Northeast China, focus on the multi-scale driving mechanism of carbon sink functions, develop new technical systems for coordinated enhancement of carbon sink for the Northeast terrestrial ecosystems, and carry out demonstrations of carbon sink enhancement technologies. These efforts will provide the scientific and technological supports for achieving the "carbon neutrality" goal.

Spatial-temporal evolution patterns of influenza incidence in Xinjiang Prefecture from 2014 to 2023 based on GIS

Using GIS technology, this study investigated the spatiotemporal distribution pattern of influenza incidence in Xinjiang from 2014 to 2023 based on influenza surveillance data. The study revealed a noticeable fluctuation trend in influenza incidence rates in Xinjiang, particularly notable spikes observed in 2019 and 2023. The results of the 3-year moving average showed a significant long-term upward trend in influenza incidence rates, confirmed by Theil-Sen method (MAD = 2.202, p < 0.01). Global spatial autocorrelation analysis indicated significant positive spatial autocorrelation in influenza incidence rates from 2016 and from 2018 to 2023 (Moran’s I > 0, P < 0.05). Local spatial autocorrelation analysis further revealed clustering patterns in different regions, with high-high clustering and low-high clustering predominating in northern Xinjiang, and low-low clustering predominating in southern Xinjiang. Hotspot analysis indicated a progressive rise in the number of influenza incidence hotspots, primarily concentrated in northern Xinjiang, particularly in Urumqi, Ili Kazakh Autonomous Prefecture, and Hotan Prefecture. Standard deviation ellipse analysis and the trajectory of influenza incidence gravity center migration showed that the transmission range of influenza in Xinjiang has been expanding, with the epidemic center gradually moving northward. The spatiotemporal heterogeneity of influenza incidence in Xinjiang highlights the need for differentiated and precise influenza prevention and control strategies in different regions to address the changing trends in influenza prevalence.

Bayesian spatio-temporal analysis of dengue transmission in Lao PDR

Dengue, a zoonotic viral disease transmitted by Aedes mosquitoes, poses a significant public health concern throughout the Lao People’s Democratic Republic (Lao PDR). This study aimed to describe spatial–temporal patterns and quantify the effects of environmental and climate variables on dengue transmission at the district level. The dengue data from 2015 to 2020 across 148 districts of Lao PDR were obtained from the Lao PDR National Center for Laboratory and Epidemiology (NCLE). The association between monthly dengue occurrences and environmental and climate variations was investigated using a multivariable Zero-inflated Poisson regression model developed in a Bayesian framework. The study analyzed a total of 72,471 dengue cases with an incidence rate of 174 per 100,000 population. Each year, incidence peaked from June to September and a large spike was observed in 2019. The Bayesian spatio-temporal model revealed a 9.1% decrease (95% credible interval [CrI] 8.9%, 9.2%) in dengue incidence for a 0.1 unit increase in monthly normalized difference vegetation index at a 1-month lag and a 5.7% decrease (95% CrI 5.3%, 6.2%) for a 1 cm increase in monthly precipitation at a 6-month lag. Conversely, dengue incidence increased by 43% (95% CrI 41%, 45%) for a 1 °C increase in monthly mean temperature at a 3-month lag. After accounting for covariates, the most significant high-risk spatial clusters were detected in the southern regions of Lao PDR. Probability analysis highlighted elevated trends in 45 districts, emphasizing the importance of targeted control strategies in high-risk areas. This research underscores the impact of climate and environmental factors on dengue transmission, emphasizing the need for proactive public health interventions tailored to specific contexts in Lao PDR.

Environmental factors affecting the distribution of deep-water rose shrimp (Parapenaeus longirostris, Lucas, 1846) abundance in the Strait of Sicily (Mediterranean Sea)

The distribution of deep-water rose shrimp (Parapenaeus longirostris, FAO 3 alpha code DPS), the main target species of demersal fisheries in the Strait of Sicily, is investigated in relation to surface parameters and biogeochemical processes. Such processes are known to influence sea bottom habitats and may be particularly relevant to the Strait of Sicily because of its relative shallowness and high surface primary production. Shrimp abundances recorded during multi-annual and seasonal trawl surveys (2004–2008) are analyzed. A GAMM and GAM model analysis is performed comparing juvenile abundances to monthly mean spatial patterns of remotely-sensed sea surface temperature (SST) and surface chlorophyll (chl), as well as their frontal structures, with a time-lag of one month, given the pelagic behavior of DPS early life stages preceding settlement. Juvenile and total shrimp abundances are also compared to the flux of particulate organic carbon (POC) to the seabed. The POC flux is computed via 1-D and 3-D models simulating sinking, re-mineralization and horizontal advection and diffusion of surface POC. The latter is derived from surface primary production maps obtained from ocean color data. Results show that the abundance of the juvenile fraction of DPS is significantly correlated with depth, distance to SST fronts and the intensity of chl fronts (correlation R2 = 80%). Furthermore, results strongly suggest the significant role of bottom POC flux in conditioning the distribution of DPS abundance, indicating that ecological processes occurring in surface waters influence food availability near the seabed in the investigated area.

Coupling Analysis between the Transonic Buffeting Flow and a Heaving Supercritical Airfoil Based on Dynamic Mode Decomposition

The coupling between a transonic buffeting flow and a supercritical airfoil with harmonic heave motion was studied. A parametric space of the heave frequency and amplitude was investigated using a verified fluid–structural interaction framework. The spatial-temporal flow pattern around the transonic airfoil was studied using dynamic mode decomposition (DMD) to unveil the physical coupling mechanism. The results show three types of flow responses under the heave motion: (I) A buffet frequency response with a λ-shape shock wave structure and recirculation zone at the shock foot. The aerodynamic performance was alike the scenario in the flow past the stationary airfoil. (II) A transitional response with a weakened shock and enhanced boundary layer. The aerodynamic performance deteriorated sharply at f=fbuffet and recovered after the frequency was past the buffet frequency. The flow pattern was characterized by a double-shock structure that interacted with the enhanced boundary layer. (III) A heave frequency response with the dominant heave motion. The variance in the aerodynamic loading increased significantly at f&gt;fbuffet and there were higher heave amplitudes in this stage. The driving motion of the airfoil transferred the energy of the buffet mode to the boundary layer with a more even energy balance according to the energy contribution analysis of the DMD modes.

Spatial variability of temperature inside atoll lagoons assessed with Landsat-8 satellite imagery

Sea Surface Temperature (SST) maps are necessary for managing marine resources in a climate change context, but are lacking for most of the 598 world's atolls. We assessed the feasibility of using the Landsat-8 (L8) satellite to infer SST maps for four French Polynesia atolls of aquaculture interest in Tuamotu Archipelago, namely Takaroa, Raroia, Tatakoto, and Reao. Specifically, we (1) used sensors to measure in situ the range of spatial temperature differences recorded in these four atoll lagoons; (2) calibrated and assessed the performances of SST algorithms to estimate lagoon temperature from L8 signals; (3) generated temperature maps for the lagoons and compared spatial patterns of temperature obtained from these maps with patterns highlighted by in situ sensors. Good agreements between satellite and in situ temperature data were obtained, with better results achieved when using an atoll-by-atoll optimization (average bias = −0.26 °C; RMSE = 0.55 °C). However, we also show that the range of temperature inside atoll lagoons is low, and of the same order of magnitude than RMSE achieved with SST algorithms. Because of the L8 overpass time (∼9 a.m.) and the revisit time (16 days), L8 SST could not capture the entire range of spatial differences measured in situ in the four lagoons, but could capture spatial gradients and fronts better than with few in situ sensors. Considering the achieved accuracies and the actual temperature differences at the four study sites, we discuss the usefulness of L8 derived SST maps to assist fishery and aquaculture management in atoll lagoons, as well as the possible generalization to other lagoons.

Impacts of different vehicle emissions on ozone levels in Beijing: Insights into source contributions and formation processes

Beijing, with the highest number of motor vehicles in China, significantly contributes to O3 pollution through substantial NOx and VOC emissions in the on-road transportation sector. Understanding the unique impact of emissions from different vehicle types on O3 levels is crucial for developing targeted strategies for O3 pollution. This study applied the Community Multiscale Air Quality Modeling System (CMAQ) to comprehensively investigate the impacts of emissions from different vehicle types on O3 levels in various regions of Beijing and to provide valuable insights into source contributions and formation processes. The results revealed that various vehicle types exhibited different spatial-temporal emission patterns, with medium-heavy duty trucks (HDT) and mini-light passenger vehicles (LDPV) identified as the primary contributors to NOx (36.1 %) and VOC (57.6 %) emissions. Using the Integrated Source Apportionment Method (ISAM) coupled in CMAQ, we found the total vehicle emissions contributed to over 20 % of daily maximum 8–h average O3 (MDA8 O3) concentration, ranked as the second largest contributor after regional transport. Contributions to O3 formation from LDPV and medium-large passenger vehicles (MDPV) were 2.6–4.0 and 4.2–6.8 ppb and mainly concentrated in urban areas, while the contributions from mini-light duty trucks (LDT) and HDT were 3.5–4.8 and 3.7–6.2 ppb and mainly concentrated in suburban areas. Through scenario analysis that removed emissions from specific types of vehicles, we found removing LDPV emissions led to decreases in daytime O3 concentration by 0.3–3.8 ppb. In contrast, removing MDPV emissions led to notable O3 increases by 4.0–11.8 ppb at rush hours. Removing LDT and HDT emissions resulted in 0.6–8.0 ppb increases in nocturnal O3 concentrations while 0.8–2.0 ppb decreases during the afternoon. This research highlights the necessity of tailoring control strategies for different vehicle types to effectively reduce O3 levels in Beijing and provides useful information for decision-makers to formulate effective measures of vehicle management in the future.

Spatial-Temporal Distribution Pattern of Dengue Fever and Spatial Relationship of the Aedes Mosquito Larvae Index in Phuket Province

Spatial-Temporal Distribution Pattern of Dengue Fever and Spatial Relationship of the Aedes Mosquito Larvae Index in Phuket Province

Characteristics of Spatial–Temporal Evolution of Carbon Emissions from Land Use and Analysis of Influencing Factors in Hubao-Eyu Urban Agglomerations, China

Exploring the dynamic relationship between land use change and carbon emissions is of great significance in promoting regional low-carbon sustainable development and “dual-carbon”. We reveal the characteristics of the evolution of spatial temporal patterns of land use carbon emissions at the county scale in resource-based urban agglomerations over the past 20 years and the analysis of influencing factors. The research results show that: (1) In terms of spatial and temporal characteristics, from 2000 to 2020, net carbon emissions from land use showed an overall upward trend, with construction land being the main source of increased carbon emissions; the spatial distribution pattern of carbon emissions shows a trend of further clustering of centers in the northeast-southwest direction, which mainly occurs in areas rich in coal resources; the economy-contributive coefficient is increasing, but ecological support coefficients are decreasing; (2) In the analysis of influencing factors, land use structure is the most significant factor contributing to the increase of carbon emissions, followed by economic level, while land use intensity per unit of GDP is the most significant factor inhibiting the increase of carbon emissions. The results of the study provide a useful reference for resource-based urban agglomerations to formulate regionally appropriate emission reduction strategies and realize low-carbon sustainable development.