Analysis Of Spatial Variability Research Articles

Seasonal Variability of Rainfall and Thunderstorm Patterns in Kenya

This paper presents an analysis of spatial and temporal variation of rainfall and thunderstorm occurrence over Kenya from January 1987 to December 2017. The meteorological data used were obtained from the Kenya Meteorological Department (KMD) for the same period. This included the monthly thunderstorm occurrences and rainfall amounts of 26 synoptic stations across the country. The characteristics of monthly, seasonal and annual frequency results were presented on spatial maps while Time series graphs were used to display the pattern for annual cycle, seasonal variations and the inter-annual variability of rainfall amounts and thunderstorm occurrences. A well-known non-parametric statistical method Mann Kendall (MK) trend test was used to determine and compare the statistical significance of the trends. Thunderstorm frequencies over the Eastern, Central and Coast regions of the country showed a bimodal pattern with high frequencies coinciding with March-April-May (MAM) and October-November-December (OND) rainy seasons. Very few thunderstorm days were detected over June-July-August (JJA) season. The areas to the western part of the country, near Lake Victoria, had the highest thunderstorm frequencies in the country over the three seasons: MAM, JJAS and OND. The annual frequency showed a quasi-unimodal pattern. These places near Lake Victoria showed significantly increasing thunderstorm trends during the MAM and OND seasons irrespective of the rainfall trends. This shows the effects of Lake Victoria over these areas, and it acts as a continuous source of moisture for thunderstorm formation. However, most stations across the country showed a reducing trend of thunderstorm frequency during MAM and JJA seasons. The importance of these findings is that they could support various policy makers, and users of climate information, especially in the agriculture and aviation industries.

Synergistic impacts of Landscape, Soil, and environmental factors on the spatial distribution of soil aggregates stability in the Danjiangkou reservoir area

Soil aggregate stability (SAS) plays a crucial role in determining the soil's resistance to erosion. Conducting a spatial variability analysis of SAS in the reservoir area, which is highly susceptible to soil degradation and erosion, can yield valuable insights for agricultural practices, landscape planning, and environmental management in this region. Nevertheless, existing research remains constrained in its capacity to accurately map and forecast soil aggregates at a spatial level. This study aims to investigate the spatial distribution of SAS in the Danjiangkou reservoir area, as well as to explore the intrinsic linkage mechanisms between changes in soil properties, environmental factors and landscape patterns and SAS. Results revealed significant spatial variations in SAS within the vicinity of the Danjiangkou reservoir area. Notably, soil properties (including clay, available potassium, soil organic matter, and available phosphorus), environmental factors (such as digital elevation model, slope, land surface temperature difference between seasons (ΔLST)), and landscape pattern (largest patch index (LPI), shannon’s diversity index (SHDI), edge density (ED)) significantly affected the spatial variability of SAS, explaining 18.91%, 8.22%, and 8.17% of the total variability of soil SAS, respectively. Structural equation modeling results indicated that altering soil properties serves as the primary approach to enhance SAS. Furthermore, interventions targeting landscape factors such as LPI, SHDI, and ED, alongside environmental factors like ΔLST and slope, exert direct influences on SAS, while also indirectly impacting it through their effects on soil properties. These findings contribute to the design of land planning and soil and water conservation measures in the reservoir watershed, promoting its protection of soil structure and its ecological service function.

Spatial and temporal variability analysis of snow cover parameters according to the urbanized area profile system

The purpose of the study is to analyze the spatial and temporal variability of snow cover parameters in the urbanized area of the city of Irkutsk and its adjacent areas according to the system of profiles reflecting the features of the area microrelief. Field surveys were conducted in accordance with standard recommendations for snow and geochemical surveys. Snow sampling was carried out at the entire snow depth, with the exception of the snow depth of 0.5-1 cm. Sampling points located at least 25 meters away from the roads. Snow sampling in the residential areas of the city was carried out in the places with undisturbed snow cover and free from extraneous snow banks and landfills. Global Mapper, Golden Software Surfer, Statistica software were used to carry out statistical analysis, build the models of profile sections and obtain the diagrams of snow cover parameter distribution. A 3D relief model was obtained on the basis of radar topographic survey data, which revealed a multiple formation relief of the studied area featuring mountains, high plains with undulating flat gently sloping watersheds, valleys, hollows and depressions. The height difference is up to 230 m. The diagrams of snow depth distribution built with regard to the profiles made it possible to identify the territories with the deepest snow cover. A geochemical analysis of the melt water filtrate was carried out over a three-year period. In 2021 a high correlation with electrical conductivity was found for the contents of tungsten, sodium, bromine, calcium, molybdenum, sulfur, barium, magnesium, antimony, tantalum, cesium, titanium, chromium, and silicon. The average correlation level with electrical conductivity was recorded for arsenic, copper, and lead. The analysis data obtained indicate a different contamination level of the area as a result of atmospheric precipitation in those years due to the activity of industrial facilities. The distribution patterns of the pH snow index have been revealed. It is shown that zones with pH<6 are mostly confined to the Irkutsk aluminum smelter. The zones with pH >6 are caused by the influence of the power plants using hydrocarbon fuels: gasoline, kerosene, fuel oil, diesel fuel, coal. The snow cover was subjected to field and laboratory studies using GIS technologies and physico-chemical methods. The distribution and migration of pollutants in various spatial and temporal aspects have been revealed taking into account the terrain relief. The conducted study opens up opportunities for modeling the landscape structure, taking into account meteorological parameters, phenological processes and snow cover state for the purposes of the national economy and the location of construction of various facilities.

Climatology of Synoptic Non-Gaussian Meteorological Anomalies in the Northern Hemisphere during 1979–2018

The analysis of spatial and temporal variability in the number of non-Gaussian extreme anomalies of climatic parameters was carried out for both the initial time series and synoptic variability in the troposphere of the Northern Hemisphere over the period 1979–2018, based on ERA-Interim reanalysis data. There are predominantly three types of empirical distribution densities at 850 hPa, each characterizing the processes of advective and convective heat transfer. At the beginning of the 21st century, compared to the end of the 20th century, there was an increase in the number of anomalies in vertical wind speed and specific humidity for the Northern Hemisphere. Additionally, there is an increase in the number of zonal wind speed anomalies in the low and middle latitudes. Regions with the maximum number of anomalies are primarily located over the continents, while for vertical wind speed anomalies, they are predominantly over the oceans. The application of R/S analysis and multifractal analysis has established that the identified tendencies (which are persistent processes) will continue in the identified regions. The time series of non-Gaussian anomalies (both initial and synoptic scales) exhibit a long-term memory of approximately four years, and synoptic extreme anomalies were found to be more predictable.

Analysis of spatial and temporal variation in grassland vegetation cover in Xinjiang section of Tianshan Mountains and the driving factors from 2001 to 2020

Analysis of spatial and temporal variation in grassland vegetation cover in Xinjiang section of Tianshan Mountains and the driving factors from 2001 to 2020

A sustainable mechanism for digitization to promote green technological innovation in enterprises

Abstract This study delves into the spatial-temporal dynamics of urban digital advancement and its green economic efficacy through methods like kernel density analysis, exploratory spatial data analysis, Gini coefficient, and coefficient of variation. Utilizing entropy weighting and regression analysis, it investigates the influence of digital development on green economic efficiency. Sustainable development performance (EWS), the volume of green patent applications (Lngrd), and other pivotal control variables are incorporated into a regression framework to elucidate the mechanisms by which digital evolution impacts green economic efficiency. Empirical findings reveal a consistent annual growth rate exceeding 5% across six regions, showcasing the steady progress and substantial achievements in digitalization. However, a wide disparity exists within enterprise digitalization levels, with the maximum digital transformation index at 4.996, a minimum of 0.002, and a median of 0.696, indicating an ongoing uneven development. Digital transformation significantly enhances corporate green technological innovation, evidenced by a coefficient of 0.014. Its effect on sustainability performance is notably more substantial in enterprises with higher equity concentrations (0.004) and greater pollution levels (0.005).

Geostatistical Insights into Trace Metal Dynamics: Techniques for Spatial Mapping, Variability Analysis, and Source Characterization in the Environment

Geostatistical Insights into Trace Metal Dynamics: Techniques for Spatial Mapping, Variability Analysis, and Source Characterization in the Environment

Spatial variation and geographical weighted regression analysis to explore open defecation practice and its determinants among households in Ethiopia.

In Ethiopia, recent evidence revealed that over a quarter (27%) of households (HHs) defecated openly in bush or fields, which play a central role as the source of many water-borne infectious diseases, including cholera. Ethiopia is not on the best track to achieve the SDG of being open-defecation-free by 2030. Therefore, this study aimed to explore the spatial variation and geographical inequalities of open defecation (OD) among HHs in Ethiopia. This was a country-wide community-based cross-sectional study among a weighted sample of 8663 HHs in Ethiopia. The global spatial autocorrelation was explored using the global Moran's-I, and the local spatial autocorrelation was presented by Anselin Local Moran's-I to evaluate the spatial patterns of OD practice in Ethiopia. Hot spot and cold spot areas of OD were detected using ArcGIS 10.8. The most likely high and low rates of clusters with OD were explored using SaTScan 10.1. Geographical weighted regression analysis (GWR) was fitted to explore the geographically varying coefficients of factors associated with OD. The prevalence of OD in Ethiopia was 27.10% (95% CI: 22.85-31.79). It was clustered across enumeration areas (Global Moran's I = 0.45, Z-score = 9.88, P-value ≤ 0.001). Anselin Local Moran's I analysis showed that there was high-high clustering of OD at Tigray, Afar, Northern Amhara, Somali, and Gambela regions, while low-low clustering of OD was observed at Addis Ababa, Dire-Dawa, Harari, SNNPR, and Southwest Oromia. Hotspot areas of OD were detected in the Tigray, Afar, eastern Amhara, Gambela, and Somali regions. Tigray, Afar, northern Amhara, eastern Oromia, and Somali regions were explored as having high rates of OD. The GWR model explained 75.20% of the geographical variation of OD among HHs in Ethiopia. It revealed that as the coefficients of being rural residents, female HH heads, having no educational attainment, having no radio, and being the poorest HHs increased, the prevalence of OD also increased. The prevalence of OD in Ethiopia was higher than the pooled prevalence in sub-Saharan Africa. Tigray, Afar, northern Amhara, eastern Oromia, and Somali regions had high rates of OD. Rural residents, being female HH heads, HHs with no educational attainment, HHs with no radio, and the poorest HHs were spatially varying determinants that affected OD. Therefore, the government of Ethiopia and stakeholders need to design interventions in hot spots and high-risk clusters. The program managers should plan interventions and strategies like encouraging health extension programs, which aid in facilitating basic sanitation facilities in rural areas and the poorest HHs, including female HHs, as well as community mobilization with awareness creation, especially for those who are uneducated and who do not have radios.

Analysis of spatial variability to develop ground investigation density guidance in New Zealand

Analysis of spatial variability to develop ground investigation density guidance in New Zealand

Adolescent marriage a violation of sexual and reproductive health rights: Trend, spatial variation and covariate analysis from national family health survey

IntroductionTen-to-Nineteen-year adolescent period is a very sensitive time for both boys and girls. Especially adolescent girls suffer more due to early marriage and its consequences of unintended pregnancy, unsafe abortion, high risk for sexually transmitted disease etc. ObjectiveThis study tried to analyse trend and spatial variation of adolescent girls' marriage practice in India. Further, this study is also tried to identify major socio-economic factors responsible for adolescent girl's marriage. MethodsPresent study utilized different rounds of National Family Health Survey (NFHS) data. Trend analysis carried out with the help of 3rd, 4th and 5th round of NFHS data. Bivariate distribution and logistic regression are used for identifying determining factors for adolescent girls’ marriage. ResultsTrend analysis result reveals the declining trend of early adolescent girls' marriage in India. However, the data reveals early girl's marriage practice is still very high. This practice is more in the states of Tripura (31%) followed by West Bengal (28%). Place of residence, wealth status, education status and mass media exposure are the main determining factors in early marriage. ConclusionMost of the states in Eastern and North-Eastern states are having high prevalence of adolescent marriage. Thus, there is an urgent need to focus on these states along with addressing socio-economic disparities. Further, there is a need of effective interventions of girl specific programmes like Beti Bachao Beti Padhao, Balika Samridhi yojana etc. working for empowerment and wellbeing of adolescent girls.

Spatial Variability and Statistical Analysis of Soil Properties in the Rice Wheat-Based Systems of North-West India

ABSTRACT There are limited reports about the impacts of long-term rice wheat cropping system (RWCS) on soil properties and nutrient stocks under smallholder farmer’s conditions in developing counties. The study was carried out in RWCS in Haryana (10 districts) of North-West India in order to map out some soil properties and assess their variability within the area. From the study area, a total of 150 surface soil samples (15 samples each district) were collected using Global Positioning System (GPS) after harvest of wheat crop. Then, soil properties, that is, pH, electrical conductivity (EC), soil organic carbon (SOC), carbonate content, cation exchange capacity (CEC), salinity content (Na+, K+, Ca2+ + Mg2+) soil available nutrients (NPK, Fe & Zn), and micronutrient fractions were measured in the laboratory. All soil properties significantly changed under RWCS, except for bulk density, SOC, and SAR variables. The SOC stock varied significantly among the majority of RWCS in north-western India but was not significantly different in total nitrogen (TN) stocks. After log-transformation of data, classical statistics were used to describe the soil properties, and one-way analysis of variance. The PCA chosen soil properties explained 81.37% of the soil quality variability among the RWCS. Such soil properties and nutrient stock variability among the RWCS suggested the introduction of suitable management practices that sustain the soil system of the RWCS with poor properties and nutrients. On the basis of the linear function, soil chemical properties (44.65%) contributed more to soil quality index (SQI) than physical ones.

Analysis of spatial variation of street landscape greening and influencing factors: an example from Fuzhou city, China

Urban street greening is an important part of urban green infrastructure, and Green View Index (GVI) is widely used to assess urban street quality and ecosystem service value as an important indicator to quantify the perception of green street landscape from a pedestrian perspective. However, the distribution of street greenery is imbalanced. Therefore, to explore the differences in street greening levels within urban cities, we crawled streetscape data using the Internet to assess the spatial distribution patterns of urban street GVI using deep learning and spatial autocorrelation, and combined 11 surrounding environmental features with multi-source geographic data to further analyze the key factors influencing the spatial variation of block GVI using ordinary least squares, geographically weighted regression (GWR) models, and multi-scale geographically weighted regression (MGWR) models. The results show that the mean value of GVI in Fuzhou city is low (23.08%), with large differences among neighborhoods and a significant spatial autocorrelation. Among the regression models, MGWR has the best fit with an R2 of 0.702, where the variables of NDVI, house price, accessibility of water bodies and parks, and the proportion of built-up land have a greater impact on GVI, and the factors do not have the same spatial effect size. The results can provide a scientific basis for promoting green visual equity in different blocks.

Respiration, phytoplankton size and the metabolic balance in the Atlantic gyres

The balance between plankton photosynthesis (GPP) and community respiration (CR) in the euphotic zone (net community production, NCP) is an essential driver of the biological carbon pump. Deficient datasets and a lack of knowledge of the mechanisms regulating CR cause poor empirical models and oversimplified parameterisations that maintain NCP as one of the most important unknowns for projections of the carbon pump. One important unresolved issue is the unexpected lack of empirical relationships between CR and the biomass or size-structure of the phytoplankton, which undermines the use of remotely sensed observations to predict net community metabolism. Here we analyse the spatial variation of plankton metabolism, chlorophyll a concentration (Chla), pico- and nanophytoplankton abundance and size-fractionated primary production (14CPP) along a latitudinal (49°N–46°S) transect of 73 stations across the Atlantic Ocean (AMT-22 cruise). The use of depth-weighted rates (rates integrated to the depth of 0.1% PAR, divided by the regionally varying depth of integration) markedly improved the depiction of latitudinal patterns and the significance of relationships, over volumetric or integrated rates. Depth-weighted CR showed clear and consistent latitudinal patterns with relevance for the distribution of NCP. Depth-weighted Chla and CR exhibited a significant relationship (CRZ=1.42ChlaZ-0.21, r2 = 0.69, N=37, p<0.001) with potential for the difficult prediction of CR. A general ratio of 1.42 mmolO2 mgChla-1 d-1 and a threshold Chla for net heterotrophy of ca. 0.25 mgChla m-3 can be tentatively proposed for the Atlantic, although further analyses of spatial and seasonal variation are necessary. We observed unusually positive NCP rates in the central part of the N gyre, due to a marked decrease of CR in a patch of high Synechococcus spp. abundance and high 14CPP by large phytoplankton. However, no relationship was observed between size-fractionated 14CPP and CR or the GPP : CR ratio during the cruise, contradicting the hypothesis that food web functioning is determined by the phytoplankton size structure. Such independence, together with the persistence of distinct GPP : CR and 14CPP : NCP relationships in distinct biogeographic provinces suggest a resilience of trophic dynamics and the existence of alternative ecosystem states, whose implications for projections of the metabolic state of the ocean are discussed.

Complex network analysis and robustness evaluation of spatial variation of monthly rainfall

Complex network analysis and robustness evaluation of spatial variation of monthly rainfall

Analysis of Spatial and Temporal Variability of Ecosystem Service Values and Their Spatial Correlation in Xinjiang, China

Xinjiang is located in arid northwest China, which is a key area for promoting the high-quality development of the regional ecological environment. In recent years, against a background of increasing human activities and rapid natural changes, Xinjiang has faced enormous ecological challenges. This paper utilizes land-use data from 2000 to 2020 to verify the region’s current state of the ecosystem. Additionally, it uses the value equivalent factor per unit area, ecosystem service value (ESV) loss and gain matrix, and double-factor spatial autocorrelation analysis to study the spatial and temporal variabilities of ESV in Xinjiang and its attribution to spatial correlation. The results show that (1) the ESV in Xinjiang exhibits an overall increasing trend during 2000–2020, with a total increase of about CNY 18.202 billion. Regulation-service ESV takes the main position in the single-service function, accounting for about 67.18% of the total ESV. In northern Xinjiang, the ESV demonstrates a decreasing trend, dropping by about CNY 16.885 billion, while in southern Xinjiang, the ESV shows an increasing trend, rising by CNY 35.086 billion. (2) For the study period, the main loss of ESV in Xinjiang is the conversion of ecological land with a high ESV into cropland or barren land with a low ESV. The conversion of bare land to grassland led to the largest increase in ESV (about CNY 209.308 billion), whereas the conversion of grassland to barren land led to the largest loss (about CNY 183.046 billion). (3) There are positive correlations among ESV, net primary productivity (NPP), and human activity intensity (HAI). However, all of the relationships weaken year by year. The spatial agglomeration of ESV ∩ NPP is significantly greater than that of ESV ∩ HAI, so NPP is the dominant factor in the spatial correlation of ESV in Xinjiang. The findings of this study provide a scientific basis for promoting high-quality regional ecological development in China’s arid northwest.

Analysis of spatial and temporal variation of vegetation NPP in Daning River Basin and its driving forces

ABSTRACT The Daning River Basin is a typical representative of the ‘mountain forest’ in the Three Gorges Reservoir (TGR) area of the Yangtze River. In recent years, with the completion of the Three Gorges Project, the local vegetation has degraded, soil erosion has become severe, and there is an urgent need to assess the environmental quality. Data were fused using the MODIS Normalized Difference Vegetation Index (NDVI) (250 m) and Landsat NDVI (30 m) through an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to obtain ESTARFM NDVI (30 m). This data was then entered into the Carnegie Ames Stanford Approach (CASA) model, along with meteorological and land use data, to calculate vegetation net primary productivity (NPP) and trends in the Daning River Basin. The detection of drivers with a high impact on vegetation NPP was done using a Geodetector. The results show that: (1) In terms of spatial and temporal changes, the annual average NPP of the watershed during the 13 years from 2008 to 2020 generally showed an upward trend, with the average yearly vegetation NPP being 512.33gC·m−2·a−1, exhibiting a low southwest to surrounding increasing trend along the river. (2) The spatial and temporal variation of vegetation NPP is influenced by several factors synergistically, with elevation, temperature, and distance from settlements being the dominant factors. The interaction of these two factors can enhance the explanatory power of vegetation NPP. Through the estimation of vegetation NPP and the analysis of influencing factors in the Daning River Basin, this study can provide a reference for the ecological restoration and management of vegetation NPP in small watersheds under similar environments.

Analysis Of The Spatial And Temporal Variability Of Rainfall In The Kayanga-Anambé Hydrological Complex (Republic Of Guinea And Senegal)

The high rainfall variability observed especially from the 1970s has intensified the vulnerability of water resources in tropical watersheds. It resulted in the recurrence of extreme hydrological events – combined with the increase in temperature which increases evapotranspiration causing considerable water losses – but also in the "Sahelization" of Sudanian areas resulting in the migration of isohyets towards the south.This article studies recent spatial and temporal changes in rainfall in the Kayanga-Anambé complex, as well as their potential hydrological implications. The methodological approach is based on (i) checking the quality of data and filling gaps using the method of rainfall indices with the Regional Vector Method (RVM); and on (ii) the analysis of spatial and temporal variability of rainfall using PETITT stationarity test and Standardized Precipitation Index (SPI).The evolution of rainfall in the basin is reflected in a fact specific to the intertropical zone, namely the variability of rainfall. This is marked by several sequences: a wet period between 1940 and 1967, followed by a long dry sequence which extends between the beginning of the 70s until the end of the 90s, and finally a slight resumption of the rainfall over the period 1993-2020. However, according to the outputs of global climate models ("Multi-model Ensemble"), the resumption of rainfall in the Kayanga basin will only be effective from 2060. This variability noted in the rains will induce a similar variability in the flows and agricultural production since they are largely dependent on it.

Analysis of Spatial and Temporal Variability and Coupling Relationship of Soil Water and Salt in Cultivated and Wasteland at Branch Canal Scale in the Hetao Irrigation District

The Hetao Irrigation District is a typical salinized irrigation district in China, and soil salinization restricts agricultural development. To explore the spatial and temporal variability of soil water and salt and the coupling relationship in the Hetao Irrigation District, a field experiment was carried out at the scale of the Yichang Irrigation District branch canal in the downstream of the Hetao Irrigation District. Fifty-three soil sampling points were established to analyze the spatial and temporal variability of soil water content and total salt content and the coupling relationship using geostatistics and the coupling degree model. The results showed that soil water content in the study area belonged to medium variability and weak variability, and soil total salt content belonged to strong variability and medium variability. The theoretical models of soil water content and total salt content semi-variance function in the study area following the Gaussian model, with the block-base ratio less than 25%, with strong spatial autocorrelation, and the spatial correlation gradually increased with the increase of soil depth. The total salt content of the soil in the study area was interpolated with higher accuracy using radial basis functions as compared to ordinary kriging interpolation. In terms of temporal changes in salinity, the average salt accumulation rate of the 0–100 cm soil layer in the study area was 20.17% when salinity increased from May to June; the average desalination rate was 16.37% when salinity decreased from June to August. The main factors affecting soil salinity in cultivated land during the growing period were irrigation, precipitation, and planting crops, and the main factors affecting soil salinity in wasteland were precipitation and topography. The average coupling degree of soil water and salt in wasteland in the study area was lower than that of cultivated land, ranging from 65.15% to 86.59% of that of cultivated land. The level of coordination is marginal coordination for cultivated land and marginal disorder for wasteland. The study provides a theoretical basis for the prevention and control of soil salinization in arid areas.

Spatial variability analysis of soil properties of Gwalior District, Madhya Pradesh

The application of fertilizers can be optimized to maintain crop yield while reducing the amount of fertilizer input. One way to achieve this is by using soil fertility maps and fertilizer recommendations to regulate fertilizer application. In this study, statistical techniques were employed to analyze the physical-chemical quality of soils in the Gwalior region of Madhya Pradesh. The study involved collecting 95 soil samples (0-15 cm) from four blocks in the Gwalior area using GPS (Bhitarwar, Morar, Ghatigao, and Dabra), and conducting laboratory analyses. The results of the analysis showed that the pH, EC, and OC of the soil samples ranged from 7.10 to 8.90, 0.21 to 0.83 dS/m, and 0.23 to 0.98%, respectively. The soil samples also had varying levels of N, P, and K, with values ranging from 194 to 336, 6.10 to 45.00, and 69.89 to 751.30 kg/ha, respectively. The study revealed significant differences in the physicochemical properties of soil in the study area

A COMPREHENSIVE ANALYSIS OF THE SPATIO-TEMPORAL VARIATION OF SATELLITE-BASED AEROSOL OPTICAL DEPTH IN MARMARA REGION OF TURKIYE DURING 2000–2021

Abstract. This study investigates the spatiotemporal variability of the aerosol optical depth (AOD) in the atmosphere over the Marmara region, Turkiye. Long-term satellite observations from MODIS MAIAC AOD data spanning the period from 2000 to 2021 are utilized. Examining the temporal variations in AOD in the Marmara region, it is observed that AOD reaches its peak during spring (May) and summer (August) months, while lower AOD values are observed in winter. Specifically, between August and December, there is a significant decline in monthly mean AOD which is majorly due to particulate removal from the atmosphere via precipitation scavenging. The findings reveal that the inter-annual variability of monthly AOD variations in the Marmara region is primarily influenced by temporary Saharan dust transportation with highest deviations from 22 year averaged AOD in late winters and early springs. The findings from the analysis of seasonal spatial variation of high AOD values revealed that the high AOD area is largest in the summer with about 54% of the total area and then spring (45%) and autumn (26%). Winter has the lowest HVA with 17% of the total area. The seasonal percentage rates of HVA are due to atmospheric conditions and aerosol sources. Larger HVA in summer is due to the increase of farming practices and biomass residue burnings combined with high moisture absorption effects and high temperature. The heating-specific emissions are the main source of anthropogenic emissions over the high AOD areas during the autumn and winter and aerosols are concentrated over the urbanized centres and industrialized zones.