Patterns Of Temporal Variation Research Articles

Longitudinal study of paralytic shellfish toxins along Canada's coast

Paralytic shellfish toxins (PST) in shellfish products have led to severe risks to human health. To monitor the risk, the Canadian Shellfish Sanitation Program has been collecting longitudinal PST measurements in blue mussel (Mytilus edulis) and soft-shell clam (Mya arenaria) samples in six coastal provinces of Canada. The spatial distributions of major temporal variation patterns were studied via Functional Principal Component Analysis. Seasonal increases in PST contamination were found to vary the most in terms of magnitude along the coastlines, which provides support for location-specific management of the time-sensitive PST contamination. In British Columbia, the first functional principal component (FPC1) indicated the variance among the magnitudes, while FPC2 indicated the seasonality of the PST levels. The temporal variations tended to be positively correlated with the abundance of dianoflagellates Alexandrium spp., and negatively with precipitation and inorganic nutrients. These findings indicate the underlying mechanism of PST variation in various geographical settings. In New Brunswick, Prince Edward, and Nova Scotia, the top FPCs indicated that the PST contamination differed mostly in the seasonal increase of the PST level during summer.

Towards spatio-temporal comparison of simulated and reconstructed sea surface temperatures for the last deglaciation

Abstract. An increasing number of climate model simulations is becoming available for the transition from the Last Glacial Maximum to the Holocene. Assessing the simulations' reliability requires benchmarking against environmental proxy records. To date, no established method exists to compare these two data sources in space and time over a period with changing background conditions. Here, we develop a new algorithm to rank simulations according to their deviation from reconstructed magnitudes and temporal patterns of orbital and millennial-scale temperature variations. The use of proxy forward modeling allows for accounting for non-climatic processes that affect the temperature reconstructions. It further avoids the need to reconstruct gridded fields or regional mean temperature time series from sparse and uncertain proxy data. First, we test the reliability and robustness of our algorithm in idealized experiments with prescribed deglacial temperature histories. We quantify the influence of limited temporal resolution, chronological uncertainties, and non-climatic processes by constructing noisy pseudo-proxies. While model–data comparison results become less reliable with increasing uncertainties, we find that the algorithm discriminates well between simulations under realistic non-climatic noise levels. To obtain reliable and robust rankings, we advise spatial averaging of the results for individual proxy records. Second, we demonstrate our method by quantifying the deviations between an ensemble of transient deglacial simulations and a global compilation of sea surface temperature reconstructions. The ranking of the simulations differs substantially between the considered regions and timescales, which suggests that optimizing for agreement with the temporal patterns of a small set of proxies might be insufficient for capturing the spatial structure of the deglacial temperature variability. We attribute the diversity in the rankings to more regionally confined temperature variations in reconstructions than in simulations, which could be the result of uncertainties in boundary conditions, shortcomings in models, or regionally varying characteristics of reconstructions such as recording seasons and depths. Future work towards disentangling these potential reasons can leverage the flexible design of our algorithm and its demonstrated ability to identify varying levels of model–data agreement. Additionally, the algorithm can be applied to variables like oxygen isotopes and climate transitions such as the penultimate deglaciation and the last glacial inception.

Temporal evolution of electron cloud in a cylindrical Penning trap at room temperature.

The temporal evolution of the electron cloud at room temperature has been recorded through a resonance circuit by observing the axial oscillation frequency of its center of mass. The electron cloud undergoes radial expansion by interacting with the residual gas molecules, and it is finally lost upon hitting the Penning trap electrodes. It has been confirmed through detailed experimental investigations that the unique temporal pattern of frequency variation is a consequence of the cloud's radial expansion. Consequently, this approach offers a non-destructive means for single-shot detection, enabling continuous monitoring of the electron cloud's radial expansion during the confinement time. This technique offers a significant advantage over its destructive alternatives.

Urban microclimate differences in continental zone of China

In modern cities, the block geometry and land cover feature directly affect the heat transfer between ground surface and atmosphere, resulting in significant differences of local microclimate. Clarifying this difference will help to accurately predict regional building energy demand and optimize regional layout design. However, existing microclimate research mainly focuses on tropical, arid, and temperate climate regions, leaving continental areas virtually unexplored. To address this problem and clarify the interactions of the urban blocks and microclimate, the combination of the local climate zone (LCZ) scheme and fixed-point weather station monitoring is employed to investigate the variation characteristics of the thermal and humidity conditions of typical urban blocks in continental zone of China. Firstly, sixty-seven typical urban blocks with self-built, national and regional meteorological stations are selected as the object of study, and classified into twelve LCZ categories, according to their inherent features. Secondly, the spatial and temporal variation patterns of the thermal environment indexes of the twelve LCZ types are analyzed and compared. Additionally, correlation analyses and regression fittings are performed between the thermal environment indexes and the urban morphological parameters. The results indicate significant differences in thermal environment indexes among different LCZ types. The mean temperature of built types is 0.72 °C higher than land cover types. The temperature and humidity exhibit a parabolic relationship with height of roughness elements and building surface fraction respectively, while the humidity has strong negatively correlations with the impervious surface fraction and the height of roughness elements.

Effects of land use and land cover change under shared socioeconomic pathways on future climate in the Yellow River basin, China

The Yellow River basin of China has experienced significant land use and land cover change (LUCC) due to excessive exploitation of nature resources, ecological degradation, and rapid urbanization, which plays an important role in the regional climate. It is crucial to explore the climate patterns of the basin under different future development scenarios to mitigate climate issues and achieve “carbon peaking and carbon neutrality goals”. Here we utilized land use and land cover (LULC) data and projections of future climate under two shared socioeconomic path – representative concentration path (SSP245 and SSP585) scenarios. Using the Weather Research and Forecasting (WRF) model, we simulated four future spatial variation patterns of temperature and precipitation in the basin. Results indicated that under the SSP245 and SSP585 scenarios, the temperature is projected to increase by 0.18 °C and 0.46 °C, respectively, while precipitation is expected to rise by 32.21 mm and 134.24 mm, respectively. The impact of LUCC was found to be relatively minor and mainly concentrated in the middle reaches of the basin. It resulted in a slight increase in temperature in both scenarios and an increase in precipitation in SSP245, but a decrease in precipitation in SSP585. Changes in farmland and urban area exhibited a certain warming effect in both scenarios, with urban areas having a greater influence, leading to a temperature increase by 0.25 °C and 0.28 °C, respectively. Forest, grassland, and bare areas had a lesser impact on temperature and showed different trends under two scenarios. Regarding precipitation, forests and urban areas had a greater influence in both SSP245 and SSP585 scenarios. This study identified the significant role of LUCC under different development scenarios in shaping future temperature and precipitation changes, providing valuable insights for effectively addressing climate issues in the Yellow River basin. It also highlights the need for clear policy recommendations and identifies institutions or agencies responsible for implementing such recommendations.

High diversity within and low but significant genetic differentiation among geographic and temporal populations of the global Streptococcus pneumoniae.

Streptococcus pneumoniae is the major cause of invasive pneumococcal disease. However, the global population structure remains largely unexplored. In this study, we investigated the spatial and temporal patterns of genetic variation of S. pneumoniae based on archived multilocus sequence typing data from PubMLST.org. Our analyses demonstrated both shared and unique distributions of sequence types (STs) and allele types among regional populations. Among the 17 915 global STs, 36 representing 15 263 isolates were broadly shared among all six continents, consistent with recent clonal dispersal and expansion of this pathogen. The analysis of molecular variance revealed that>96% genetic variations were found within individual regional populations. However, though low (<4%), statistically significant genetic differentiation among regional populations was observed. Comparisons between non-clone-corrected and clone-corrected datasets showed that localized clonal expansion contributed significantly to the observed genetic differentiations among regions. Temporal analyses of the isolates showed that implementation of pneumococcal conjugate vaccine impacted the distributions of STs, but the effect on population structure was relatively limited. Linkage disequilibrium analyses identified evidence for recombination in all continental populations; however, the inferred recombination was not random. We discussed the limitations and implications of our analyses to the global epidemiology and future vaccine developments for S. pneumoniae.

Deep-Learning-Based Automatic Extraction of Aquatic Vegetation from Sentinel-2 Images—A Case Study of Lake Honghu

Aquatic vegetation is an important component of aquatic ecosystems; therefore, the classification and mapping of aquatic vegetation is an important aspect of lake management. Currently, the decision tree (DT) classification method based on spectral indices has been widely used in the extraction of aquatic vegetation data, but the disadvantage of this method is that it is difficult to fix the threshold value, which, in turn, affects the automatic classification effect. In this study, Sentinel-2 MSI data were used to produce a sample set (about 930 samples) of aquatic vegetation in four inland lakes (Lake Taihu, Lake Caohai, Lake Honghu, and Lake Dongtinghu) using the visual interpretation method, including emergent, floating-leaved, and submerged vegetation. Based on this sample set, a DL model (Res-U-Net) was used to train an automatic aquatic vegetation extraction model. The DL model achieved a higher overall accuracy, relevant error, and kappa coefficient (90%, 8.18%, and 0.86, respectively) compared to the DT method (79%, 23.07%, and 0.77) and random forest (78%,10.62% and 0.77) when utilizing visual interpretation results as the ground truth. When utilizing measured point data as the ground truth, the DL model exhibited accuracies of 59%, 78%, and 91% for submerged, floating-leaved, and emergent vegetation, respectively. In addition, the model still maintains good recognition in the presence of clouds with the influence of water bloom. When applying the model to Lake Honghu from January 2017 to October 2023, the obtained temporal variation patterns in the aquatic vegetation were consistent with other studies. The study in this paper shows that the proposed DL model has good application potential for extracting aquatic vegetation data.

Diversity of an Odonata assemblage from a tropical dry forest in San Buenaventura, Jalisco, Mexico (Insecta, Odonata).

The patterns of richness, diversity, and abundance of an odonate assemblage from San Buenaventura, Jalisco are presented here. A total of 1087 specimens from seven families, 35 genera and 66 species were obtained through monthly samplings of five days each during a period of one year. Libellulidae was the most diverse family (28 species), followed by Coenagrionidae (21), Gomphidae (7), Aeshnidae (6), Calopterygidae (2), Lestidae (1) and Platystictidae (1). Argia was the most speciose genus. The highest species richness and Shannon diversity were found during August and September, whereas the highest abundance was observed in June and the highest Simpson diversity was recorded in September - all of which were associated with the rainy season. The highest values of phylogenetic diversity were found from June to October. The different diversity facets of this assemblage were positively correlated with precipitation and minimum temperature, whereas maximum temperature showed no influence. In addition, we found that this odonate diversity was higher than most Mexican localities with tropical dry forest (TDF) studied. We continue our efforts to describe the patterns of richness, diversity and abundance of some insect groups associated with the tropical dry forest ecosystem in Mexico, following a latitudinal gradient of the distribution of this ecosystem in the country. Our emphasis here was to evaluate the spatial and temporal patterns of richness and diversity of an Odonata assemblage from Jalisco, Mexico.

Patterns in the temporal complexity of global chlorophyll concentration

Decades of research have relied on satellite-based estimates of chlorophyll-a concentration to identify oceanographic processes and plan in situ observational campaigns; however, the patterns of intrinsic temporal variation in chlorophyll-a concentration have not been investigated on a global scale. Here we develop a metric to quantify time series complexity (i.e., a measure of the ups and downs of sequential observations) in chlorophyll-a concentration and show that seemingly disparate regions (e.g., Atlantic vs Indian, equatorial vs subtropical) in the global ocean can be inherently similar. These patterns can be linked to the regularity of chlorophyll-a concentration change and the likelihood of anomalous events within the satellite record. Despite distinct spatial changes in decadal chlorophyll-a concentration, changes in time series complexity have been relatively consistent. This work provides different metrics for monitoring the global ocean and suggests that the complexity of chlorophyll-a time series can be independent of its magnitude.

The Co-occurrence Matrix and the Correlation Network of Phytophagous Insects Are Driven by Abiotic and Biotic Variables: the Case of Canola.

Co-occurrence a correlation profiles are driven by different factors (exogenous and endogenous) and drawing a profile of association between species based on co-occurrence, without assessing how these species vary in terms of ecological niche can lead to wrong conclusions. The objective was to determine the co-occurrence and correlation patterns of phytophagous insects in canola crop and to evaluate how these patterns varied according to the crop stage (phenology-biotic) and sowing times (agricultural practice-abiotic). We found that the patterns of co-occurrence and correlation between species were reflections of population variations due to the phenology and sowing times of canola. Variations in the multi-species abundance matrix were influenced by mean air temperature and accumulated rainfall. The main species associated with canola in southern Brazil, in terms of abundance, were P. xylostella, D. speciosa, and N. viridula. These species were mostly negatively associated. When evaluating their population variations, we found that they explore different temporal niches, whether in terms of phenology or sowing times. Finally, we demonstrate empirically that despite being important, association patterns based on co-occurrence and correlation should be interpreted in light of the understanding of patterns of niche exploitation and temporal variation of species.

A comprehensive prediction model of drilling wellbore temperature variation mechanism under deepwater high temperature and high pressure

Comprehensively considering the impact of properties of drilling fluid at high temperature and high pressure (HTHP), drill string eccentricity, rotation, and rock breaking of drill bit on the annular temperature profile, transient temperature variations were predicted by using a wellbore heat transfer model. The model was numerically solved using the finite difference method. Compared to DRILLBENCH simulations, the predicted model was more consistent with the field-measured data. The calculations revealed that comprehensively considering various heat source factors made the annular temperature closer to the actual wellbore value, with a temperature difference of up to 10.66 °C. When considering variations of properties of drilling fluid at HTHP, the lower section of the annulus showed a significant decrease in temperature with depth, with a bottom-hole temperature difference of 5.4 °C. The bottom-hole temperature decreased by about 2.6 °C from concentric to eccentric drill string, and the pressure difference was about 1 MPa. Compared to drill string eccentricity, rotation and rock breaking of drill bit had a greater impact on annular temperature, with bottom-hole temperature differences of 13 °C and 22 °C, respectively. Accurate temperature modeling can better understand and predict the variation patterns of wellbore temperature, providing important guidance for the development of deepwater drilling.

Nitrate Pollution Characteristics and Its Quantitative Source Identification of Major River Systems in China

Accurate source identification/apportionment is essential for optimizing water NO3--N pollution control strategies. This study conducted a meta-analysis based on data from 167 rivers across China from 2000 to 2022 to analyze the spatial and temporal variation patterns of nitrate pollution in seven major river systems and to quantitatively identify the source composition of riverine nitrate. The average ρ（NO3--N） in the seven major river systems was （4.54±3.99） mg·L-1， with 9.6% of river ρ（NO3--N） exceeding 10 mg·L-1. The riverine ρ（NO3--N） in eastern China were higher than that in western China， and the highest concentration was observed in the Haihe River system. Additionally， tributaries experienced more serious NO3--N pollution than that in the main stream. The ρ（NO3--N） in most river systems in the dry season was higher than that in the wet season， except in the Yellow River system. There was significant nitrification in the Pearl River system， the middle and lower reaches of the Yellow River system， the middle reaches of the Liaohe River system， the Songhua River system， and the Haihe River system， whereas there was significant denitrification in the Yangtze River system， the Huaihe River system， and the lower reaches of the Pearl River system. Based on the dual stable isotopes-based MixSIAR model， the major NO3--N source was sewage/manure （ &gt; 50%） in the Yangtze River system， Haihe River system， Liaohe River system， and Southeast River system. Soil nitrogen was the main NO3--N source in the Songhua River system （56.4%）， and the contribution of fertilizer nitrogen， soil nitrogen， and sewage/manure to NO3--N pollution in the Pearl River system， Huai River system， and Yellow River system was 20%-40%. The contribution rate of sewage/manure to NO3--N in the tributaries was higher than that in the main stream， whereas the contribution rate of soil nitrogen to NO3--N in the main stream was higher than that in the tributaries. The contribution rate of soil nitrogen， fertilizer nitrogen， and atmospheric deposition nitrogen to nitrate nitrogen in the wet season was higher than that in the dry season， whereas the contribution rate of sewage/manure to NO3--N pollution in the dry season was higher than that in the wet season. Therefore， point source pollution such as domestic and production sewage discharge should be controlled in the Haihe River system， the Yangtze River system， the Liaohe River system， the tributaries and the downstream main stream areas of Yellow River system， and the downstream area of the Pearl River system， whereas non-point source pollution caused by the loss of fertilizer and soil nitrogen should be controlled in the Huaihe River system， the Songhua River system， the middle reaches of the main stream area of the Yellow River system， and the middle and upper reaches of the Pearl River system. The results can provide a scientific basis for the effective control of nitrate pollution in the river systems in China.

Diurnal patterns of spatial stream temperature variations reveal the need for integrating thermal heterogeneity in riverscape habitat restoration

Longer durations of warmer weather, altered precipitation, and modified streamflow patterns driven by climate change are expected to impair ecosystem resilience, exposing freshwater ecosystems and their biota to a severe threat worldwide. Understanding the spatio-temporal temperature variations and the processes governing thermal heterogeneity within the riverscape are essential to inform water management and climate adaptation strategies. We combined UAS-based imagery data of aquatic habitats with meteorological, hydraulic, river morphology and water quality data to investigate how key factors influence spatio-temporal stream heterogeneity on a diurnal basis within different thermal regions of a large recently restored Danube floodplain. Diurnal temperature ranges of aquatic habitats were larger than expected and ranged between 14.2 and 28.0 °C (mean = 20.7 °C), with peak median temperatures (26.1 °C) around 16:00 h. The observed temperature differences in timing and amplitude among thermal regions were unexpectedly high and created a mosaic pattern of temperature heterogeneity. For example, cooler groundwater-influenced thermal regions provided several cold water patches (CWP, below 19.0 °C) and potential cold water refuges (CWRs) around 12:00 h, at the time when other habitats were warmer than 21.0 °C, exceeding the ecological threshold (20.0 °C) for key aquatic species. Within the morphological complexity of the restored floodplain, we identified groundwater influence, shading and river morphology as the key processes driving thermal riverscape heterogeneity. Promoting stream thermal refuges will become increasingly relevant under climate change scenarios, and river restoration should consider both measures to physically prevent habitat from excessive warming and measures to improve connectivity that meet the temperature requirements of target species for conservation. This requires restoring mosaics of complex and dynamic temperature riverscapes.

Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022.

During the SARS-CoV-2 pandemic, governments and public health authorities collected massive amounts of data on daily confirmed positive cases and incidence rates. These data sets provide relevant information to develop a scientific understanding of the pandemic's spatiotemporal dynamics. At the same time, there is a lack of comprehensive approaches to describe and classify patterns underlying the dynamics of COVID-19 incidence across regions over time. This seriously constrains the potential benefits for public health authorities to understand spatiotemporal patterns of disease incidence that would allow for better risk communication strategies and improved assessment of mitigation policies efficacy. Within this context, we propose an exploratory statistical tool that combines functional data analysis with unsupervised learning algorithms to extract meaningful information about the main spatiotemporal patterns underlying COVID-19 incidence on mainland Portugal. We focus on the timeframe spanning from August 2020 to March 2022, considering data at the municipality level. First, we describe the temporal evolution of confirmed daily COVID-19 cases by municipality as a function of time, and outline the main temporal patterns of variability using a functional principal component analysis. Then, municipalities are classified according to their spatiotemporal similarities through hierarchical clustering adapted to spatially correlated functional data. Our findings reveal disparities in disease dynamics between northern and coastal municipalities versus those in the southern and hinterland. We also distinguish effects occurring during the 2020-2021 period from those in the 2021-2022 autumn-winter seasons. The results provide proof-of-concept that the proposed approach can be used to detect the main spatiotemporal patterns of disease incidence. The novel approach expands and enhances existing exploratory tools for spatiotemporal analysis of public health data.

Sampling through space and time: multi-year analysis reveals dynamic population genetic patterns for an amphibian metapopulation

Metapopulations are dynamic, and population genetics can reveal both spatial and temporal metapopulation variation. Yet, population genetic studies often focus on samples collected within a single time period or combine samples taken across time periods due to limited resources and the assumption that these approaches capture patterns and processes occurring over decadal and longer temporal scales. However, this may leave important fine-scale temporal variation in genetic composition undetected, particularly for metapopulations in which dynamic populations are expected. We investigated temporal patterns of population genetic diversity, effective population size, and differentiation across three sample periods for a dryland amphibian metapopulation. We sampled nine distinct Arizona treefrog (Hyla (Dryophytes) wrightorum) breeding ponds in 2014, 2018/2019, and 2021 and genotyped 17 microsatellite loci to quantify spatial and temporal population genetic dynamics. Genetic diversity within and between populations varied significantly among years. Most notably, we identified a concerning decline in allelic richness across populations, with an average − 26.11% difference between a population’s first and last sample period. Effective population sizes were generally small (Ne < 100) and variable within and among populations over time, with many populations falling below common conservation thresholds by the final sample period. Trends in global genetic diversity, as measured by heterozygosity, and population differentiation were relatively consistent across all sampling periods. Overall, we found that “snapshot” or single-time sampling approaches may miss temporal variability in genetic composition that has important conservation implications, including early warning signs of decline in genetic diversity.

Analyzing daily change patterns of indoor temperature in district heating systems: A clustering and regression approach

Measuring the indoor temperature of building rooms is a valuable approach for evaluating thermal comfort and providing feedback control for heat substations in district heating systems (DHSs) in China. Previous studies on indoor temperatures have primarily focused on analyzing their overall trends and influencing factors, while research on daily change patterns is lacking. This study utilized a clustering method to analyze the indoor temperature data from an actual DHS in Northeast China. First, a 24-h observation vector was constructed using the deviation between the actual and target values to represent the daily temperature pattern. Second, the k-means method was applied to cluster the values, and the quantity distribution and typical characteristics of each cluster were analyzed. Finally, a multi-nominal logistic regression model was used to analyze the influence of different factors on each cluster. The comparison results with the four representative clustering algorithms indicated that k-means was the optimal model and the optimal number of clusters was 4. The trend of each cluster was roughly the same, with the main difference being the fluctuation amplitude and distance from the target value. The differences between the clusters were related to various influencing features, with the primary return pressure for workdays and the secondary return pressure for holidays being the most significant. This study identified the optimal daily variation patterns of indoor temperature and analyzed the important features that affect this pattern, which is beneficial for enhancing the regulatory efficiency of DHS.

The role of habitat heterogeneity and timescales on the recovery of reef epifaunal communities affected by a massive oil spill in the tropics

From August 2019 to January 2020 the Brazilian Coast was impacted by the largest oil spill in the Tropical oceans ever since. Paiva Beach, one of the most preserved sandy beaches in the northeast tropical Brazilian coast, was among the most affected regions by the oil spill in October 2019. This area has important reef systems that harbor abundant macroalgal assemblages with very diverse epifaunal communities. The present study aims to evaluate the impacts of the 2019 oil spill on epifauna associated with the seaweed species Jania capillacea and Penicillus capitatus collected in Paiva Beach from July 2019 to October 2022, and use the interaction macroalgae/epifauna/timescale as a proxy to quantify the effects of oil spills on communities structure and recovery. The epifauna of both algae did not suffer severe changes in abundance immediately after the spill, however, the abundance of taxa like echinoderms, sea spiders (Pycnogonida) and peracarid crustaceans dropped during or soon after the event, whereas others, like sabellid worms, had strong increase in abundance, possibly related to the spill. Both phytal assemblages also had distinct temporal patterns in species diversity and abundance, the last being strongly correlated to amphipod abundance for some taxa. The epifauna of J. capillacea had a short-term tendency of decrease in taxonomic diversity, whereas the epifaunal communities of P. capitatus gained a few taxa in the months immediately after the disaster. The diversity profiles of both communities returned to pre-spill conditions a few months later, with a slower abundance recovery in J. capillacea. Local environmental characteristics like landscape heterogeneity, connectivity with other environments, and critical-species (mainly amphipods) were possibly the main buffering factors for the phytal communities after the oil spill.

Temporal Patterns of Insect Pollinator Diversity in Relation to Flowering Plant Phenology in the Western Mindanao State University Experimental Forest Area, Upper La Paz, Zamboanga City, Philippines

Temporal Patterns of Insect Pollinator Diversity in Relation to Flowering Plant Phenology in the Western Mindanao State University Experimental Forest Area, Upper La Paz, Zamboanga City, Philippines

Modeling study of PM2.5 pollution episode of early spring 2019 in Hokkaido, Japan caused by biomass burning in Northeast China

Biomass burning (BB), in particular agricultural waste burning (Agri-BB), occurs at random locations, scales, and times. These factors make it challenging to detect Agri-BB accurately through satellite observations. Thus, the BB emission inventories using satellite observation data have uncertainties for their emission estimation approach and cause poor model performance for air pollutants including PM2.5. We utilized the two BB emission inventories, GFEDv4.1s and FINNv2.5 with the CMAQ model to simulate the PM2.5 heavy pollution episode in Hokkaido 2019. To estimate Agri-BB contributions, we conducted three simulation cases for each BB emission inventory: with and without Agri-BB emission, and the boosted Agri-BB emission cases. The baseline simulation failed to capture the temporal and spatial variation patterns of PM2.5. Meanwhile, the boosted Agri-BB case could show favorable performance for PM2.5 concentrations. These results indicated that the two BB emission inventories underestimated Agri-BB emissions. In the two boosted Agri-BB cases, the PM2.5 contributions from Agri-BB accounted for more than 50% during the episode. Moreover, high PM2.5 emissions were found in Northeast China and its surrounding regions similar to the two boosted Agri-BB cases. Consequently, the results revealed that Agri-BB emissions during the episode were significantly derived from the agricultural areas in Northeast China.

Applying Geo-Spatial Techniques to Determine the Temporal Variation and Conversion Pattern of Land-Use Surrounding Negombo Lagoon

Remote Sensing technology and Geographic Information Systems are the major partners in geospatial technology and they play a vital role in surface, subsurface and above earth’s studies. It provides a better platform for researchers specially in the fields of regional planning, disaster management, natural resources management etc. Observing land-use patterns in a particular area forms a basis for decision making in these fields. The study has the purpose of applying geo-spatial techniques to determine the temporal variation and conversion pattern of land-use surrounding Negombo lagoon. For that, the study has used Landsat imageries of 2003, 2009, 2015 and 2021 from the United States Geological Survey. ISO Cluster Unsupervised Classification method and data conversion tools were used to create initial layers for analysing the land-use in selected four years by using Arc GIS 10.4. software. Intersect and field calculators were used to get a better understanding of the variation of land-use through classified Landsat imageries of respective years. The results will support for measuring area of the conversion of different land-use into another and / or within the same category in a particular area throughout the time. The study concluded that, the used techniques could measure only the temporal variation of major land use types; water bodies, vegetation, built-up areas and cultivations but the minor categories of land uses were unable to detect. However, the better spatial and temporal resolution data enables to improve results to avoid such issues.