Spatial Trend Analysis Research Articles

The spatiotemporal and dependency analysis of selected meteorological parameters and normalized difference vegetation index with aerosol optical depth over east Africa

The unprecedented rise in atmospheric aerosols, coupled with their intricate interactions with the environment through a wide array of physical, chemical, and biological processes, has profoundly impacted global climate. Their presence in the atmosphere scatters and absorbs solar radiation, thus altering the amount of sunlight reaching the Earth's surface. These direct effects, along with the indirect effects of aerosols, have significantly altered atmospheric temperatures, land surface processes, global surface temperature, hydrological cycle, and ecosystems. Understanding the complex interplay between aerosols and climatic variables necessitates a multidisciplinary approach, such as dependency modeling. Addressing these challenges, the current study conducts a spatiotemporal correlational analysis of selected key meteorological parameters with aerosol optical depth over East Africa (EA) using multisensory data from Moderate-resolution Imaging Spectroradiometer (MODIS), Modern-Era Retrospective analysis for Research and Application (MERRA-2) model, and Tropical Rainfall Measurement Mission (TRMM). Employing a weighted least squares regression (WLS) model, the study quantifies trends in the time series of climatic variables and Normalized Difference Vegetation Index (NDVI), further utilizing a statistical dependency modeling technique for correlational analysis. The trend analysis reveals a significant decreasing trend in surface wind speed (SWS) in most months, with sporadic positive trends attributed to anthropogenic activities, notably biomass burning, observed in January. Spatial trend analysis of Precipitation Rate (PR) displays heterogeneity, with significant negative trends in January and March, and positive trends in February, April, November, and December. Negative trends during May to August are attributed to increased anthropogenic activities, while enhanced positive trends in May correlate with low aerosol optical depth (AOD) during this period. Surface air temperature (SAT) exhibits diverse variations across the region, with dry months recording higher averages and trends than wet months. The study notes heterogeneous correlations in NDVI over the study area, with positive and negative correlations observed in different regions. Specifically, positive correlations are noted along the coastal and Lake Victoria regions, attributed to improved PR enhancing vegetation cover in these areas.

Study on the Level Measurement and Spatial Distribution of Comprehensive Competitiveness of Logistics Industry in Henan Province

This paper takes the 18 cities in Henan Province as the research object, combining their relevant data in 2022, and conducts a comparative study on the comprehensive competitiveness indicators of logistics industry in Henan Province through factor analysis method. Starting from the three dimensions of infrastructure level, economic benefits, and development scale, this paper explores the level of comprehensive competitiveness of logistics industry in Henan Province and explores the spatial distribution characteristics of comprehensive competitiveness through spatial trend analysis and natural breakpoint method. And it puts forward corresponding improvement strategies.

Analysis of Spatial and Temporal Trends of Vegetation Cover Evolution and Its Driving Forces from 2000 to 2020—A Case Study of the WuShen Counties in the Maowusu Sandland

The WuShen counties in the hinterland of the Maowusu Sandland are located in the “ecological stress zone” of the forest–steppe desert, with low vegetation cover, a strong ecosystem sensitivity, and poor stability under the influence of human activities. Therefore, it is important to study and analyze the changes in vegetation growth in this region for the purpose of objectively evaluating the effectiveness of desertification control in China’s agricultural and pastoral intertwined zones, and formulating corresponding measures in a timely manner. In this paper, the spatial and temporal variations in the vegetation NDVI in the WuShen counties of the Maowusu Sandland and their response relationships with driving factors were investigated by using a trend test, center of gravity transfer model, partial correlation calculation, and residual analysis, and by using the MOD13A3 vegetation NDVI time series data from 2000 to 2020, as well as the precipitation, temperature, and potential evapotranspiration data from the same period. The results showed the following: ① The regional vegetation NDVI did not fluctuate significantly with latitude and longitude, and the NDVI varied between 0.227 and 0.375 over the 21-year period, with a mean increase of 0.13 for the region as a whole and an increase of 0.61 for the region of greatest change. Of the area, 86.83% experienced a highly significant increase, and the trend in increase around rivers and towns was higher than that in the northwestern inland flow area, with the overall performance of “low in the west and high in the east”. ② Only 2.07% of the vegetation NDVI center of gravity did not shift, and the response with climate factors was mainly characterized by having consistent or opposite center of gravity changes with precipitation and potential evapotranspiration. ③ Human activities have been the dominant factor in the vegetation NDVI change, with 75.89 percent of the area positively impacted by human activities, and human activities in the southwest inhibiting the improvement of vegetation in the area. The impact of human activities on the unchanged land type area is increasing, most obviously in the farmland area, and the impact of human activities on the changed land type area is gradually decreasing in the area where the farmland becomes impervious. The vegetation in the area above 1300 m above sea level is degraded by the environment and human activities. The research results can provide scientific support for the implementation of ecological fine management and the formulation of corresponding ecological restoration and desertification control measures in the Maowusu Sandland. At the same time, it is expected to serve as a baseline for other studies on the evolution of vegetation in agro-pastoral zones.

The Practice of Urban Planning In Managing Urban Expansion: The Case of Level-I Towns in Southern Ethiopia

The world’s urbanization continues to grow rapidly occurring in developing regions. The rapid pace of urbanization without equivalent economic growth causes uneven spatial dispersion, land speculation, urban sprawl, ecological damage, and socio-cultural chaos. Urban planning is a key tool in managing such urbanization and particularly urban expansion challenges through several strategies. The objective of this study is to evaluate the practice of urban planning in managing urban expansion by applying both quantitative and qualitative research approaches. The first-hand data was collected by a structured questionnaire survey, interview, and physical observation, whereas secondary data was analyzed from spatial and non-spatial data of the study towns, national urban policy and strategy documents, and the national urban development spatial plan. Descriptive analysis, principal factor analysis, spatial trend analysis, and content analysis techniques were applied with the help of the SPSS and GIS analysis tools to reach the findings. The practices of urban planning in managing the urban expansion of the study town were evaluated on the basis of factors including urban spatial growth management, community engagement, economic change, and peri-urban communities’ understanding of urban planning. The major findings indicated that these variables are poorly practical, and their failure has been collectively posing significant obstacles to the effectiveness of urban planning. This implies that the urban planning practice in managing urban expansion was insignificant in the study towns. Therefore, it is best to use the alternative expansion area planning strategy, that promotes planned spatial growth, community inclusivity, mixed economic activity, environmental sustainability, and the community’s awareness of expansion areas.

Groundwater nitrate contamination in China: Spatial distribution, temporal trend, and driver analysis

China's groundwater is facing a significant threat from nitrate pollution. Here we analyzed 2348 regional surveys of groundwater nitrate levels in China from 1990 to 2020, examining distribution, trends, and drivers. This study uncovers a concerning rise in nitrate pollution, with estimated median nitrate levels climbing from 3.84 mg/L in 1990 to 6.94 mg/L in 2020. A stark contrast is observed between regions: the northern areas have a median nitrate concentration of 8.54 mg/L, significantly higher than the southern regions, where the median is just 7.15 mg/L. From 1990 to 2020, agricultural activity consistently emerges as the dominant driver of changes in groundwater nitrate concentrations, while groundwater exploitation, domestic pollution, and industrial production also contribute to varying degrees. This analysis highlights the urgency for region-specific policies and interventions to address the escalating nitrate pollution in China's groundwater.

Liver Cancer Mortality Disparities at a Fine Scale Among Subpopulations in China: Nationwide Analysis of Spatial and Temporal Trends.

China has the highest number of liver cancers worldwide, and liver cancer is at the forefront of all cancers in China. However, current research on liver cancer in China primarily relies on extrapolated data or relatively lagging data, with limited focus on subregions and specific population groups. The purpose of this study is to identify geographic disparities in liver cancer by exploring the spatial and temporal trends of liver cancer mortality and the years of life lost (YLL) caused by it within distinct geographical regions, climate zones, and population groups in China. Data from the National Death Surveillance System between 2013 and 2020 were used to calculate the age-standardized mortality rate of liver cancer (LASMR) and YLL from liver cancer in China. The spatial distribution and temporal trends of liver cancer were analyzed in subgroups by sex, age, region, and climate classification. Estimated annual percentage change was used to describe liver cancer trends in various regions, and partial correlation was applied to explore associations between LASMR and latitude. In China, the average LASMR decreased from 28.79 in 2013 to 26.38 per 100,000 in 2020 among men and 11.09 to 9.83 per 100,000 among women. This decline in mortality was consistent across all age groups. Geographically, Guangxi had the highest LASMR for men in China, with a rate of 50.15 per 100,000, while for women, it was Heilongjiang, with a rate of 16.64 per 100,000. Within these regions, the LASMR among men in most parts of Guangxi ranged from 32.32 to 74.98 per 100,000, whereas the LASMR among women in the majority of Heilongjiang ranged from 13.72 to 21.86 per 100,000. The trend of LASMR varied among regions. For both men and women, Guizhou showed an increasing trend in LASMR from 2013 to 2020, with estimated annual percentage changes ranging from 10.05% to 29.07% and from 10.09% to 21.71%, respectively. Both men and women observed an increase in LASMR with increasing latitude below the 40th parallel. However, overall, LASMR in men was positively correlated with latitude (R=0.225; P<.001), while in women, it showed a negative correlation (R=0.083; P=.04). High LASMR areas among men aligned with subtropical zones, like Cwa and Cfa. The age group 65 years and older, the southern region, and the Cwa climate zone had the highest YLL rates at 4850.50, 495.50, and 440.17 per 100,000, respectively. However, the overall trends in these groups showed a decline over the period. Despite the declining overall trend of liver cancer in China, there are still marked disparities between regions and populations. Future prevention and control should focus on high-risk regions and populations to further reduce the burden of liver cancer in China.

Spatial and temporal trend analysis of temperature extremes in Tanzania

This study examines the effects of extreme temperature events, defined explicitly as the 98th percentile temperature for heat, in Tanzania from 1981 to 2023. The study utilises percentile analysis, z-score calculations, and trend mapping to highlight the prevalence of extreme temperatures. The z-score analysis examines the occurrence of extreme temperatures and their distribution in terms of frequency and space. It identified Tanzania&amp;rsquo;s mid-eastern and north-western regions as areas with a high concentration of extreme temperatures over the previous 43 years. The study analyses the length of extreme occurrences and reveals changing patterns. In 2020, the events were shorter, while in 2021 and 2022, they became longer-lasting, and there was a notable concentration of extended anomalies near the northern coastline. In 2023, the highest temperatures ever recorded were observed, with more than half of the regions seeing prolonged periods of extreme temperatures lasting for a week and significant deviations from the norm staying for four weeks or more. This was particularly notable in the southern regions of Lindi and Mtwara. The harmonic trend analysis of temperature shows an upward temperature trend noticeably in regions such as Arusha, Kilimanjaro, Kagera, Morogoro, Simiyu, and Shinyanga and is somewhat inconspicuous in most areas.

Spatial and trend analysis of gestational syphilis cases in Brazil from 2011 to 2020: an ecological study

ObjectivesTo analyze the rate of gestational syphilis (GS) based on temporal trends over 11 years, as well as the spatial distribution of GS in Brazil, based on the identification of spatial clusters.MethodologyAn ecological, using Brazil and its regions as an analysis unit, based on gestational syphilis data reported in the Notifiable Diseases Information System (SINAN), from 2011 to 2020. Thematic maps were built for spatial data analysis, and the Prais-Winsten autoregressive model was used to verify the trend. Spatial analysis identified the distribution of clusters (high-high; low-low; high-low and low-high) of distribution of GS across Brazilian municipalities, using a 5% significance level.ResultsGestational syphilis experienced a considerable increase in cases during the studied period, with a peak of 37,436 cases in 2018. The spatial distribution of the disease is heterogeneous in the country. A growing trend was observed in all states of Brazil, except for Espírito Santo, where it remained stationary, with a monthly variation of 10.32%.ConclusionThe spatial and temporal trend analysis point to syphilis as an important public health problem. The numbers are alarming and show the urgent need for measures to prevent and control syphilis during pregnancy.

Spatial and Temporal Trend Analysis of Precipitation and Temperature Data in Malwa Region of Kshipra River Basin, India

Spatial and Temporal Trend Analysis of Precipitation and Temperature Data in Malwa Region of Kshipra River Basin, India

Temporal and spatial trend analysis of all-cause depression burden based on Global Burden of Disease (GBD) 2019 study

Depression has been reported as one of the most prevalent psychiatric illnesses globally. This study aimed to obtain information on the global burden of depression and its associated spatiotemporal variation, by exploring the correlation between the global burden of depression and the social development index (SDI) and associated risk factors. Using data from the Global Burden of Disease study from 1990 to 2019, we described the prevalence and burden of disease in 204 countries across 21 regions, including sex and age differences and the relationship between the global disease burden and SDI. The age-standardized rate and estimated annual percentage change were used to assess the global burden of depression. Individuals with documented depression globally ranged from 182,183,358 in 1990 to 290,185,742 in 2019, representing an increase of 0.59%. More patients experienced major depressive disorder than dysthymia. The incidence and disability-adjusted life years of depression were the highest in the 60–64 age group and much higher in females than in males, with this trend occurring across all ages. The age-standardized incidence and adjusted life-years-disability rates varied with different SDI levels. Relevant risk factors for depression were identified. National governments must support research to improve prevention and treatment interventions.

Spatial and temporal trend analysis of rainfall in Nagaland (India) using machine learning techniques

Spatial and temporal trend analysis of rainfall in Nagaland (India) using machine learning techniques

Spatial trend analysis of significant wave heights in the Kara Sea

Over the past decades, the extent of sea ice in the Arctic, including the Kara Sea, has been diminishing. This phenomenon has a direct impact on wind waves as the increased expansion of ice-free water influences wave height. Furthermore, alterations in the ice cover also lead to modifications in atmospheric circulation, necessitating a concurrent analysis of wind and waves to refine the understanding of their interrelationships. In this study, wave modeling data were employed using the WAVEWATCH III model and NCEP/CFSR/CFSv2 reanalyzes. Calculations were performed on a non-structural computational grid. The grid covers the Barents and Kara Seas, as well as the entire northern part of the Atlantic Ocean. The spatial resolution varies from ~ 700 m for the coastal zone of the Kara Sea, to ~ 20 km in the open part of the Kara Sea, covering the period from January 1, 1979 to December 31, 2021. Subsequently, average significant wave heights (SWH), maximum SWH, and the 95th percentile of SWH were computed for each grid node on both monthly and yearly basis. The annual values were analyzed for trends and their significance. Calculations were conducted for both the entire period and ice-free period. Positive trends in annual mean values were observed throughout the sea, with the maximum trend occurring near the boundary with the Barents Sea, barely exceeding 0.2 m/10 years. The northern and northeastern parts of the sea were characterized by significant positive trends of the maximum SWH values. Maximum trend values for the 95th percentile of SWH were also evident in the northern part of the Kara Sea. For the ice-free period, maximum trend values were notable for both the annual mean and the 95th percentile of SWH in the northern part of the sea (maximum trend values are approximately 0.25 m/10 years and 0.5 m/10 years, respectively). Significant positive trends in the annual mean SWH were characteristic of the southern part of the sea, while the largest and significant trends for maximum wave heights were observed in the northeast. The assessment of the contribution of wind and ice regimes to the variability of wind waves remains a subject of discussion.

Historical Climate Trends and Extreme Weather Events in the Tri-State Area: A Detailed Analysis of Urban and Suburban Differences

This study analyzes daily temperature and precipitation data collected from 44 weather stations throughout New York, New Jersey, and Connecticut to assess and quantify the historical climatic changes within these states. The study conducts a detailed examination of spatial and temporal trends, focusing on specific stations that best represent the climatic diversity of each area. A critical analysis aspect involves comparing temperature trends in urban and suburban areas, mainly focusing on New York City. The findings reveal a significant upward increasing trend in average temperatures across all seasons, with urban areas, especially NYC, exhibiting the most marked increases. This trend is notably sharp in the spring, reflecting climate change’s escalating influence. The study also observes an increase in the annual average temperatures and a concurrent decrease in the variability of temperature ranges, suggesting a stabilization of temperature fluctuations over time. Also, we identified a notable increase in heat wave frequency, more so in urban locales than in their suburban counterparts. Analysis of precipitation patterns, particularly in NYC, reveals a decline in snowfall days, consistent with the general warming trend. The results demonstrate significant trends in seasonal average temperatures, a decrease in the variability of temperatures, and a rise in heat wave occurrences, with urban areas typically experiencing warmer conditions. This comprehensive study highlights the need for a more in-depth analysis of spatial precipitation trends. It underscores the importance of continued research in understanding the multifaceted impacts of climate change, particularly in differentiating urban and rural experiences.

Policy implications for synergistic management of PM2.5 and O3 pollution from a pattern-process-sustainability perspective in China

In recent years, the pattern of air pollution in China has changed profoundly, and PM2.5 and surface ozone (O3) have become the main air pollutants affecting the air quality of cities and regions in China. The synergistic control of the two has become the key to the sustainable improvement of air quality in China. In this study, we investigated and analyzed the spatial and temporal distribution patterns, exposure health risks, key drivers, and sustainable characteristics of PM2.5 and O3 concentrations in China from 2013 to 2022 at the national and city cluster scales by combining methodological models such as spatial statistics, trend analysis, exposure-response function, Hurst index, and multi-scale geographically weighted regression (MGWR) model. Ultimately, a synergistic management system for PM2.5 and O3 pollution was proposed. The results showed that: (1) The PM2.5 concentration decreased at a rate of 1.45 μg/m3 per year (p < 0.05), while the O3 concentration increased at a rate of 2.54 μg/m3 per year (p < 0.05). The trends of the two concentrations showed significant differences in spatial distribution. (2) Population exposure risks to pollutants showed an increasing trend, with PM2.5 and O3 increasing by 55.1 % and 42.7 %, respectively. The annual deaths associated with exposure to PM2.5 and O3 demonstrated a decreasing and inverted U-shaped trend, respectively, with annual average deaths of 1.312 million and 98,000. Significant regional disparities in health risks from these pollutants were influenced by socio-economic factors such as industrial activities and population density. In the future, it is expected that more than half of China's regions will be exposed to rising risks of PM2.5 and O3 population exposure. (3) Key drivers of regional exacerbation in PM2.5 and O3 levels include the number of industrial enterprises above designated size (NSIE) and population agglomeration (PA), while the disposable income of urban residents (URDI), technological innovation (TI), and government attention level (GAL) emerged as primary factors in controlling pollution hotspots, ranked in order of influence from greatest to least as TI > GAL > URDI. Overall, this study sheds light on the current status of air pollution and health risk sustainability in China and enhances the understanding of future air pollution dynamics in China. The results of the study may help to develop effective targeted control measures to synergize the management of PM2.5 and O3 in different regions.

Land Use Land Cover Change Detection and Future Forecasting in Ningxia, China: A Random Forest and ANN-Based Approach for Sustainable Development

The accurate tracking and prediction of Land Use Land Change monitoring and prediction precision or accuracy are urgent for technological development and practical ecology management. This objective is pursued by understanding of the changing patterns of LULC temporally and spatially in Ningxia, China, a semi-arid region experiencing desertification and over-rapid urbanization. Therefore, one of the main objectives of this work is prediction for the future trends within this field of research. This research utilized the MODIS data taken from the satellite Moderate Resolution Imaging Spectroradiometer through remote sensing and advanced machine learning algorithms. During 2003 to 2023, the random forest classifier was used to monitor the LULC change in Ningxia, passing to different stage and increasingly affluent cities. These surveys predict the possible scenarios, including the rapid increase in desertification, urbanization side and erosion of the areas for agriculture, forestry and water-covered. A merging approach based on cellular automata and artificial neural network employed with the purpose of typing the future condition of land use and land cover. This approach relies on calibration to study future operations that remain unaffected by peoples’ choice. The calibrated version could be done to become tightly related to the spatial variables that heavily shift in time. The altered model achieved a great accuracy of 85% similarly the maximum value of 0.96 for the Kappa coefficient, which indicates the discovery of the ANN. Deserts and town are a few of the surfaces that have grown under science. As a result, wild animals’ facilities and natural flora have deteriorated, lowering agricultural output. (1) According to the transition probability matrix, this could be as high as 28% from agricultural land to desert. (2) One of the findings from spatial analytical trend analysis that I mentioned above, is also detected in terms of a distinct rise accumulation just for the desert category with respect to all other LULC types combined. (3) Thus, further increases in desert and artificial land might be coupled with urbanization (e. g.) to climate change.

Spatial-temporal analysis of hepatitis E in Hainan Province, China (2013-2022): insights from four major hospitals.

Exploring the Incidence, Epidemic Trends, and Spatial Distribution Characteristics of Sporadic Hepatitis E in Hainan Province from 2013 to 2022 through four major tertiary hospitals in the Province. We collected data on confirmed cases of hepatitis E in Hainan residents admitted to the four major tertiary hospitals in Haikou City from January 2013 to December 2022. We used SPSS software to analyze the correlation between incidence rate and economy, population density and geographical location, and origin software to draw a scatter chart and SAS 9.4 software to conduct a descriptive analysis of the time trend. The distribution was analyzed using ArcMap 10.8 software (spatial autocorrelation analysis, hotspot identification, concentration, and dispersion trend analysis). SAS software was used to build an autoregressive integrated moving average model (ARIMA) to predict the monthly number of cases in 2023 and 2024. From 2013 to 2022, 1,922 patients with sporadic hepatitis E were treated in the four hospitals of Hainan Province. The highest proportion of patients (n = 555, 28.88%) were aged 50-59 years. The annual incidence of hepatitis E increased from 2013 to 2019, with a slight decrease in 2020 and 2021 and an increase in 2022. The highest number of cases was reported in Haikou, followed by Dongfang and Danzhou. We found that there was a correlation between the economy, population density, latitude, and the number of cases, with the correlation coefficient |r| value fluctuating between 0.403 and 0.421, indicating a linear correlation. At the same time, a scatter plot shows the correlation between population density and incidence from 2013 to 2022, with r2 values fluctuating between 0.5405 and 0.7116, indicating a linear correlation. Global Moran's I, calculated through spatial autocorrelation analysis, showed that each year from 2013 to 2022 all had a Moran's I value >0, indicating positive spatial autocorrelation (p < 0.01). Local Moran's I analysis revealed that from 2013 to 2022, local hotspots were mainly concentrated in the northern part of Hainan Province, with Haikou, Wenchang, Ding'an, and Chengmai being frequent hotspot regions, whereas Baoting, Qiongzhong, and Ledong were frequent cold-spot regions. Concentration and dispersion analysis indicated a clear directional pattern in the average density distribution, moving from northeast to southwest. Time-series forecast modeling showed that the forecast number of newly reported cases per month remained relatively stable in 2023 and 2024, fluctuating between 17 and 19. The overall incidence of hepatitis E in Hainan Province remains relatively stable. The incidence of hepatitis E in Hainan Province increased from 2013 to 2019, with a higher clustering of cases in the northeast region and a gradual spread toward the southwest over time. The ARIMA model predicted a relatively stable number of new cases each month in 2023 and 2024.

Long-term spatial and temporal trend analysis of stream flow and suspended sediment transport of Godavari River Basin, India

Long-term spatial and temporal trend analysis of stream flow and suspended sediment transport of Godavari River Basin, India

Shallow groundwater characterisation and hydrograph classification in the coastal city of Ōtautahi/Christchurch, New Zealand

Groundwater is present at shallow depth under many coastal low-lying cities. Despite the importance of protecting coastal urbanised areas from flooding and climate-change-induced sea-level rise, the effects of shallow groundwater fluctuations are rarely investigated. The aim of this study was to determine characteristics of shallow groundwater, including spatial and temporal trends in depths to groundwater and their relationship to natural and anthropogenic stressors. The study uses depth to groundwater measurements from a uniquely extensive and densely spaced monitoring network in Ōtautahi/Christchurch, New Zealand. Data-driven analysis approaches were applied, including spatial interpolation, autocorrelation, clustering, cross-correlation and trend analysis. These approaches are not commonly applied for groundwater assessments despite the potential for them to provide insights and information for city-wide systems. The comprehensive approach revealed discernible clusters and trends within the dataset. Responses to stresses such as rainfall events and stream flow were successfully classified using clustering analysis. The time series analysis indicated that in areas of shallow groundwater, low variation in levels occurred and this was also found using clustering. However, attributing some clusters to specific hydrogeological attributes or stressors posed challenges. The primary feature in hydrograph classification proved to be the proximity to tidal rivers and their correlation with tidal signals. These results highlight the value of using large datasets to characterise spatial and temporal variability of shallow groundwater in urban coastal settings and to assist with monitoring infrastructure planning in the face of future climate-change hazards.

Comprehensive Evaluation of Spatial Distribution and Temporal Trend of NO2, SO2 and AOD Using Satellite Observations over South and East Asia from 2011 to 2021

The past decade has witnessed remarkable economic development, marked by rapid industrialization and urbanization across Asian regions. This surge in economic activity has led to significant emissions, resulting in alarming levels of air pollution. Our study comprehensively assessed the spatial and temporal trends of key pollutants, namely nitrogen dioxide (NO2), sulfur dioxide (SO2), and aerosol (using aerosol optical depth (AOD) at 550 nm as an indicator), from 2011 to 2021. The data sources utilized include OMI onboard the Aura satellite for NO2 and SO2, as well as MODIS onboard Terra and Aqua satellites for AOD. The results from spatial and temporal trend analyses of the three parameters show that there is a clear declining trend over China and Republic of Korea (e.g., NO2 is declining with an overall rate of −7.8 × 1012 molecules/cm2/year over China) due to the strict implementation of air pollution control policies. However, it is essential to note that both countries still grapple with substantial pollution levels, with proportions exceeding 0.5, indicating that air quality is improving but has not yet reached a safe threshold. In contrast, South Asian regions, including Bangladesh, Pakistan, and India, are experiencing an increasing trend (e.g., NO2 is increasing with an overall rate of 1.2 × 1012 molecules/cm2/year in Bangladesh), primarily due to the lack of rigorous air pollution control policies. The average emissions of NO2 and SO2 were remarkably higher in winter than in summer. Notably, the identified hotspots are statistically significant and predominantly coincide with densely populated areas, such as the North China Plain (NCP). Furthermore, this study underscores the pivotal role of sector-wise emissions in air quality monitoring and improvement. Different cities are primarily influenced by emissions from specific sectors, emphasizing the need for targeted pollution control measures. The findings presented in this research contribute valuable insights to the air quality monitoring and improvement efforts in East and South Asian regions.

EVALUASI KEBERMANFAATAN KEBIJAKAN PEMBANGUNAN INFRASTRUKTUR JALAN TOL TRANS JAWA (CIPALI) BERDASARKAN TINJAUAN ASPEK EKONOMI DAN LINGKUNGAN

National development aims to improve people's welfare and encourage economic growth. Cipali toll road has become one of Java's main distribution routes for goods and public transportation. This policy paper proposes to provide an overview of the impact and benefits of the construction of the Cikopo-Palimanan toll road on economic and environmental aspects and to develop policy recommendations to overcome the issues/problems faced. The analysis approach carried out in this research is first, Statistical Trend Analysis and Spatial Trend Analysis of land use via the Google Earth platform with the hotspot analysis method. The results show that the Cipali Toll Road constructions can improve the economy of areas affected by development at a macro level with an average LPE above 5%. However, there were changes in land use in the period 2010-2022, which resulted in the area of rice fields decreasing from 1,001 hectares to 999 hectares. One of the expected policy recommendations is the synchronization of toll road construction policies with policies to increase national food crop production, through the development of toll road alternatives.